Stochastics Books

Book SynopsisNow in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics.Key features in new edition:* 35 new exercises* Expanded section on the algebra of sets * Expanded chapters on probabilities to include more classical examples* New section on regression* Online instructors' manual containing solutions to all exercises<Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications.Review of the first edition: This textbook is a classical and well-written introduction to probability theory and statistics. … the book is written ‘for an audience such as computer science students, whose mathematical background is not very strong and who do not need the detail and mathematical depth of similar books written for mathematics or statistics majors.’ … Each new concept is clearly explained and is followed by many detailed examples. … numerous examples of calculations are given and proofs are well-detailed." (Sophie Lemaire, Mathematical Reviews, Issue 2008 m)Trade Review“Schay (emer., Univ. of Massachusetts) has created a text for a two semester, calculus-based course in mathematical statistics. … The prose reads well. Physical production is good. … Summing Up: Recommended. Upper-division undergraduates and graduate students.” (W. R. Lee, Choice, Vol. 54 (6), February, 2017)“I believe that students concentrating in mathematics and related subjects will find this book readable and interesting. … I think that students learning the probability for the first time will get real value out of working through the examples and exercises of the text. … Introduction to Probability with Statistical Applications is very clearly written and reading the book is enjoyable. I would certainly recommend Schay’s book as a primary textbook for an undergraduate course in calculus-based probability.” (Jason M. Graham, MAA Reviews, September, 2016)Table of ContentsIntroduction.- The Algebra of Events.- Combinatorial Problems.- Probabilities.- Random Variables.- Expectation, Variance, Moments.- Some Special Distributions.- The Elements of Mathematical Statistics.

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Book SynopsisThis book contains all of Wolfgang Doeblin's publications. In addition, it includes a reproduction of the pli cacheté on l'équation de Kolmogoroff and previously unpublished material that Doeblin wrote in 1940. The articles are accompanied by commentaries written by specialists in Doeblin's various areas of interest. The modern theory of probability developed between the two World Wars thanks to the very remarkable work of Kolmogorov, Khinchin, S.N. Bernstein, Romanovsky, von Mises, Hostinsky, Onicescu, Fréchet, Lévy and others, among whom one name shines particularly brightly, that of Wolfgang Doeblin (1915–1940). The work of this young mathematician, whose life was tragically cut short by the war, remains even now, and indeed will remain into the future, an exemplar of originality and of mathematical power. This book was conceived and in essence brought to fruition by Marc Yor before his death in 2014. It is dedicated to him.Trade Review“It attracted interest not only by mathematicians or some probability theory specialists, but also by the public, which normally would not be that interested in mathematics. … the editors gathered not only all published articles of W. Doeblin, but also some more papers, especially the ‘cahier jaune’ (yellow notebook). In comments and annotations the contents of the papers are put into today’s context by probability theory specialists. The book is excellent, a large and substantial work.” (Silke Göbel, zbMATH 1460.01019, 2021)Table of ContentsJ.-M. Bismut, Avant-propos.- H. Föllmer, Geleitwort.- B. Bru, Preface.- Part I The Life of Wolfgang Doeblin March 17, 1915 – June 21, 1940: B. Bru, Biographical Summary.- W. Doeblin, Autobiographical Note.- W. Doeblin (with remarks by B. Bru and E. Seneta), Notice sur les travaux.- List of Works of Wolfgang Doeblin.- Conversion Table.- T. Lindvall, W. Doeblin 1915–1940.- B. Bru and M. Yor, La vie de Doeblin et le pli cacheté 11.668.- B. Bru and M. Yor, Comments on the Life and Mathematical Legacy of Wolfgang Doeblin.-B. Bru and E. Seneta, Two Letters of W. Doeblin to A. N. Kolmogorov.- Part II Markov Chains: E. Seneta, Doeblin on Discrete Markov Chains.- M. Iosifescu, On Doeblin and Fortet’s paper “Sur des chaînes à liaisons complètes”.- M. Iosifescu, On Doeblin’s paper “Remarques sur la théorie métrique des fractions continues”.- E. Nummelin, Doeblin’s Theory of Markov Chains on a General Measurable State Space.- W. Doeblin, Sur les chaînes discrètes de Markoff [1936b].- W. Doeblin, Errata 1: Sur les chaînes discrètes de Markoff [1936c].- W. Doeblin, Errata 2: Sur les chaînes discrètes de Markoff [1936d].- W. Doeblin, Sur les chaînes de Markoff [1936e].- W. Doeblin and R. Fortet, Sur deux notes de MM. Kryloff et Bogoliouboff [1937e].- W. Doeblin, Éléments d’une théorie générale des chaînes constantes simples de Markoff [1937f].- W. Doeblin, Le cas discontinu des probabilités en chaîne [1937a].- W. Doeblin, Sur le cas continu des probabilités en chaîne [1937b].- W. Doeblin, and Robert Fortet Sur des chaînes à liaisons complètes [1937c].- W. Doeblin, Sur l’équation de Smoluchowsky [1937d].- W. Doeblin, Sur les propriétés asymptotiques de mouvements régis par certains types de chaînes simples [1938g] (reprint of [1937g] and [1937h]).- W. Doeblin, Sur l’équation matricielle A(t+s) = [A(t)A(s)] et ses applications aux probabilités en chaîne [1938a].-W. Doeblin, Sur l’équation matricielle A(t+s) = [A(t)A(s)] et ses applications au calcul des probabilités [1940a].- W. Doeblin, Sur deux problèmes de M. Kolmogoroff concernant les chaînes dénombrables [1938f].- W. Doeblin, Exposé de la théorie des chaînes simples constantes de Markoff à un nombre fini d’états [1938e].- W. Doeblin, Remarques sur la théorie métrique des fractions continues [1940d.- W. Doeblin, Éléments d’une théorie générale des chaînes simples constantes de Markoff [1940e].- Part III Sums of Independent Random Variables: D. M. Mason, Notes on Wolfgang Doeblin’s 1940 paper: L’ensemble de puissances d’une loi de probabilité.- W. Doeblin and P. Lévy, Sur les sommes de variables aléatoires indépendantes à dispersions bornées inférieurement [1936a].- W. Doeblin, Premiers éléments d’une étude systématique de l’ensemble de puissances d’une loi de probabilité [1938b].- W. Doeblin, Étude de l’ensemble de puissances d’une loi de probabilité [1938c].- W. Doeblin, Sur les sommes d’un grand nombre de vecteurs aléatoires [1938d].- W. Doeblin, Sur un problème de calcul des probabilités [1939b].- W. Doeblin, Sur les sommes d’un grand nombre de variables aléatoires indépendantes [1939c].- W. Doeblin, Sur l’ensemble de puissances d’une loi de probabilité (1940) [1940f].- W. Doeblin (with remarks by B. Bru and E. Seneta), Sur l’ensemble de puissances d’une loi de probabilité [1946].- Part IV Chapman’s Equations. Part IV.1 Documents Published Before the pli cacheté: W. Doeblin (with a remark by B. Bru), Sur certaines mouvements aléatoires discontinus [1939d].- W. Doeblin, Sur l’équation de Kolmogoroff [1938h].- W. Doeblin, Sur certains mouvements aléatoires [1939a].- W. Doeblin, Sur l’équation de Kolmogoroff [1940b].- W. Doeblin, Sur des mouvements mixtes [1940c].- Part IV.2 The pli cacheté: M. Yor, Présentation du pli cacheté.- W. Doeblin (with remarks by Bernard Bru and Marc Yor), Sur l’équation de Kolmogoroff, pli cacheté déposé le 26 février 1940, ouvert le 18 mai 2000 [1940g/2000].- B. Bru, Notes de lecture du pli cacheté.- W. Doeblin (with remarks by Bernard Bru), Exposé(s) sur l’équation de Chapman [1938i/2000].- Part IV.3 Archival Documents Related to the pli cacheté: B. Bru, Introduction to Doeblin’s “Sur la solution de M. Hostinský de l’équation de Chapman”.- W. Doeblin, Sur la solution de M. Hostinský de l’équation de Chapman [1940h/1993].- W.Doeblin (with an introduction by B. Bru and E. Seneta), Le cahier jaune: Recherche sur l’équation de Chapman. Propriétés communes aux mouvements régis par l’équation de Chapman [1940i/2020].- Part V Appendix: B. Bru and E. Seneta, Postface.- Acknowledgements.- Credits.- Bibliography.- Supplementary Reading.

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Book SynopsisThis book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Data sets are taken from real experiments and sample SAS programs are included with each chapter. Experimental design is an essential part of investigation and discovery in science; this book will serve as a modern and comprehensive reference to the subject.Trade Review“The textbook provides a practically oriented version of design and analysis of experiments. The corresponding methods are illustrated by means of numerous simple experiments. Thus, the models and methods are equipped with many examples, exercises, numerical results and related tables and figures. ... The present volume can be recommended as textbook for lectures on models and methods of experimental design as well as handbook for use in practice.” (Kurt Marti, zbMATH 1383.62001, 2018)Table of ContentsPrinciples and Techniques.- Planning Experiments.- Designs With One Source of Variation.- Inferences for Contrasts and Treatment Means.- Checking Model Assumptions.- Experiments With Two Crossed Treatment Factors.- Several Crossed Treatment Factors.- Polynomial Regression.- Analysis of Covariance.- Complete Block Designs.- Incomplete Block Designs.- Designs With Two Blocking Factors.- Confounded Two-Level Factorial Experiments.- Confounding in General Factorial Experiments.- Fractional Factorial Experiments.- Response Surface Methodology.- Random Effects and Variance Components.- Nested Models.- Split-Plot Designs

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Book SynopsisThe emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book.Trade Review“This is a book that any discrete proababilist will want to have on the shelf. It is a comprehensive extension of the author's masterfully written text Markov Chains ... Surprisingly; the book contains an extensive amount of information theory. ... In my opinion the new book would be ideal for a year-long course on discrete probability.” (Yevgeniy Kovchegov, Mathematical Reviews, May, 2018)“This is a very carefully and well-written book. The real pleasure comes from the contents but also from the excellent fonts and layout. Graduate university students and their teachers can benefit a lot of reading and using this book. There are more than good reasons to strongly recommend the book to anybody studying, teaching and/or researching in probability and its applications.” (Jordan M. Stoyanov, zbMATH 1386.60003, 2018) “This book is an excellent piece of writing. It has the strictness of a mathematical book whose traditional purpose is to state and prove theorems, and also has the features of a book on an engineering topic, where solved and unsolved exercises are provided. I appreciated the very carefully selected solved examples that are interwoven in each chapter. They provide an indispensable aid to digest the concepts and methods presented.” (Dimitrios Katsaros, Computing Reviews, February, 21, 2018) “This is a comprehensive volume on the application of discrete probability to combinatorics, information theory, and related fields. It is accessible for first-year graduate students. … Results are easy to find and reasonably easy to understand. … Summing Up: Recommended. Graduate students and faculty.” (M. Bona, Choice, Vol. 54 (12), August, 2017)Table of ContentsIntroduction.- 1.Events and probability.- 2.Random variables.- 3.Bounds and inequalities.- 4.Almost-sure convergence.- 5.Coupling and the variation distance.- 6.The probabilistic method.- 7.Codes and trees.- 8.Markov chains.- 9.Branching trees.- 10.Markov fields on graphs.- 11.Random graphs.- 12.Recurrence of Markov chains.- 13.Random walks on graphs.- 14.Asymptotic behaviour of Markov chains.- 15.Monte Carlo sampling.- 16. Convergence rates.- Appendix.- Bibliography.

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Book SynopsisDieses umfassende Lehr-und Nachschlagewerk für Naturwissenschaftler und Ingenieure vermittelt dem Leser zentrale Teile der Wahrscheinlichkeitstheorie, der Theorie stochastischer Prozesse sowie der mathematischen Statistik.Table of Contents0 Einführung.- 1 Wahrscheinlichkeitstheorie.- 1.1 Zufällige Ereignisse.- 1.2 Wahrscheinlichkeit zufälliger Ereignisse.- 1.3 Bedingte Wahrscheinlichkeit und Unabhängigkeit.- 1.4 Diskrete Zufallsgrößen.- 1.4.1 Grundlagen.- 1.4.2 Parametrische Kenngrößen.- 1.4.3 Diskrete Wahrscheinlichkeitsverteilungen.- 1.4.4 Momenterzeugende Funktionen.- 1.5 Stetige Zufallsgrößen.- 1.5.1 Grundlagen.- 1.5.2 Parametrische Kenngrößen.- 1.5.3 Nichtnegative Zufallsgrößen.- 1.5.4 Stetige Wahrscheinlichkeitsverteilungen.- 1.5.4.1 Normalverteilung (Gaußsche Verteilung).- 1.5.4.2 Logarithmische Normalverteilung.- 1.5.4.3 Inverse Gaußverteilung.- 1.5.4.4 Weibuliverteilung.- 1.5.4.5 Erlangverteilung.- 1.5.4.6 Gammaverteilung.- 1.5.4.7 Betaverteilung.- 1.5.5 Momenterzeugende Funktionen.- 1.6 Funktionen einer Zufallsgröße.- 1.7 Simulation von Zufallsgrößen.- 1.8 Mehrdimensionale Zufallsgrößen.- 1.8.1 Zweidimensionale Zufallsgrößen.- 1.8.1.1 Gemeinsame Wahrscheinlichkeitsverteilung.- 1.8.1.2 Unabhängige Zufallsgrößen.- 1.8.1.3 Bedingte Verteilung.- 1.8.1.4 Funktionen zweier Zufallsgrößen.- 1.8.1.5 Abhängigkeitsmaße für zwei Zufallsgrößen.- 1.8.1.6 Zweidimensionale Normalverteilung.- 1.8.1.7 Diskrete zweidimensionale Zufallsgrößen.- 1.8.2 n-dimensionale Zufallsgrößen.- 1.8.2.1 Grundlagen.- 1.8.2.2 Summen von Zufallsgrößen.- 1.8.2.3 n-dimensionale Normalverteilung.- 1.9 Ungleichungen in der Wahrscheinlichkeitstheorie.- 1.9.1 Abschätzungen für Wahrscheinlichkeiten.- 1.9.1.1 Ungleichungen vom Markov-Tschebyschev-Typ.- 1.9.1.2 Exponentialabschätzungen.- 1.9.1.3 Ungleichungen fur Maxima von Summen.- 1.9.2 Ungleichungen und Abschätzungen für Momente.- 1.10 Grenzwertsätze in der Wahrscheinlichkeitstheorie.- 1.10.1 Konvergenzarten.- 1.10.2 Gesetze der großen Zahlen.- 1.10.2.1 Schwache Gesetze der großen Zahlen.- 1.10.2.2 Starke Gesetze der großen Zahlen.- 1.10.3 Zentraler Grenzwertsatz.- 1.10.4 Lokale Grenzwertsätze.- 1.11 Charakteristische Funktionen.- 1.11.1 Komplexe Zufallsgrößen.- 1.11.2 Eigenschaften charakteristischer Funktionen.- 1.11.3 Charakteristische Funktion diskreter Zufallsgrößen.- 2 Stochastische Prozesse.- 2.1 Einführung.- 2.2 Kenngrößen stochastischer Prozesse.- 2.3 Eigenschaften stochastischer Prozesse.- 2.4 Spezielle stochastische Prozesse.- 2.4.1 Stochastische Prozesse mit stetiger Zeit.- 2.4.2 Stochastische Prozesse mit diskreter Zeit.- 2.5 Poissonsche Prozesse.- 2.5.1 Homogener Poissonprozess.- 2.5.1.1 Definition und Eigenschaften.- 2.5.1.2 Homogener Poissonprozess und Gleichverteilung.- 2.5.2 Inhomogener Poissonprozess.- 2.6 Erneuerungsprozesse.- 2.6.1 Grundlagen.- 2.6.2 Erneuerungsfunktion.- 2.6.2.1 Erneuerungsgleichungen.- 2.6.2.2 Abschätzungen der Erneuerungsfunktion.- 2.6.3 Rekurrenzzeiten.- 2.6.4 Asymptotisches Verhalten.- 2.6.5 Stationäre Erneuerungsprozesse.- 2.6.6 Alternierende Erneuerungsprozesse.- 2.6.7 Kumulative stochastische Prozesse.- 2.6.8 Regenerative stochastische Prozesse.- 2.7 Markovsche Ketten mit diskreter Zeit.- 2.7.1 Grundlagen und Beispiele.- 2.7.2 Klassifikation der Zustände.- 2.7.2.1 Abgeschlossene Zustandsmengen.- 2.7.2.2 Äquivalenzklassen.- 2.7.2.3 Periodizität.- 2.7.2.4 Rekurrenz und Transienz.- 2.7.3 Grenzwertsätze und stationäre Verteilung.- 2.7.4 Geburts- und Todesprozesse.- 2.8 Markovsche Ketten mit stetiger Zeit.- 2.8.1 Grundlagen.- 2.8.2 Kolmogorovsche Gleichungen.- 2.8.3 Stationäre Zustandswahrscheinlichkeiten.- 2.8.4 Konstruktion Markovscher Systeme.- 2.8.5 Erlangsche Phasenmethode.- 2.8.6 Geburts- und Todesprozesse.- 2.8.6.1 Zeitabhängige Zustandswahrscheinlichkeiten.- 2.8.6.2 Stationäre Zustandswahrscheinlichkeiten.- 2.8.6.3 Verweildauern.- 2.8.7 Semi-Markovsche Prozesse.- 2.9 Martingale.- 2.9.1 Martingale in diskreter Zeit.- 2.9.2 Martingale in stetiger Zeit.- 2.10 Wiener Prozess.- 2.10.1 Definition und Eigenschaften.- 2.10.2 Niveauüberschreitung.- 2.10.3 Transformationen des Wiener Prozesses.- 2.10.3.1 Elementare Transformationen.- 2.10.3.2 Ornstein-Uhlenbeck-Prozess.- 2.10.3.3 Wiener Prozess mit Drift.- 2.10.3.4 Integraltransformationen.- 2.11 Spektralanalyse stationärer Prozesse.- 2.11.1 Grundbegriffe.- 2.11.2 Prozesse mit diskretem Spektrum.- 2.11.3 Prozesse mit stetigem Spektrum.- 2.11.3.1 Spektralzerlegung der Kovarianzfunktion.- 2.11.3.2 Spektralzerlegung des Prozesses.- 3 Mathematische Statistik.- 3.1 Stichproben und ihre empirische Auswertung.- 3.1.1 Stichproben.- 3.1.2 Häufigkeits- und Summenhäufigkeitsverteilung.- 3.1.3 Empirische Punktschätzung.- 3.1.3.1 Mittelwertsmaße.- 3.1.3.2 Streuungsmaße.- 3.1.4 Graphische Anpassung einer empirischen Verteilung an eine theoretische Verteilung.- 3.2 Punktschätzung.- 3.2.1 Eigenschaften von Schätzfunktionen.- 3.2.2 Schätzmethoden.- 3.2.2.1 Maximum-Likelihood-Methode.- 3.2.2.2 Momentenmethode.- 3.2.3 Wahrscheinlichkeitsverteilungen von Schätzfunktionen.- 3.2.3.1 Stichprobenverteilungen.- 3.2.3.2 Extremwertverteilungen.- 3.3 Intervallschätzung.- 3.3.1 Grundlagen.- 3.3.2 Konfidenzintervalle für Parameter der Normalverteilung.- 3.3.2.1 Konfidenzintervall für den Erwartungswert (Varianz bekannt).- 3.3.2.2 Konfidenzintervall für den Erwartungswert (Varianz unbekannt).- 3.3.2.3 Konfidenzintervall für die Varianz.- 3.3.3 Approximative Konfidenzintervalle.- 3.3.3.1 Konfidenzintervall für eine Wahrscheinlichkeit.- 3.3.3.2 Konfidenzintervall für den Erwartungswert einer poissonverteilten Zufallsgröße.- 3.4 Parametertests.- 3.4.1 Grundlagen.- 3.4.2 Tests über Parameter der Normalverteilung.- 3.4.2.1 Test über den Erwartungswert bei bekannter Varianz.- 3.4.2.2 Test über den Erwartungswert bei unbekannter Varianz.- 3.4.2.3 t-Test für verbundene Stichproben.- 3.4.2.4 Test auf Gleichheit der Erwartungswerte zweier Zufallsgrößen.- 3.4.2.5 Test auf Gleichheit der Varianzen.- 3.4.3 Approximative Tests.- 3.4.3.1 Test über eine Wahrscheinlichkeit.- 3.4.3.2 Vergleich zweier Wahrscheinlichkeiten.- 3.5 Verteilungsfreie Tests.- 3.5.1 Anpassungstests.- 3.5.1.1 Chi-Quadrat-Anpassungstest.- 3.5.1.2 Kolmogorov-Smirnov-Test.- 3.5.2 Tests auf Homogenität.- 3.5.2.1 Vorzeichentest.- 3.5.2.2 Wilcoxon-Vorzeichen-Rang-Test.- 3.5.2.3 Zwei-Stichproben-Rang-Test von Wilcoxon (-Mann-Whitney).- 3.5.2.4 Zwei-Stichproben-Iterationstest von Wald-Wolfowitz.- 3.5.2.5 Chi-Quadrat-Homogenitätstest.- 3.5.3 Chi-Quadrat-Unabhängigkeitstest.- 3.6 Korrelationsanalyse.- 3.6.1 Einführung.- 3.6.2 Einfacher Korrelationskoeffizient.- 3.6.3 Rangkorrelationskoeffizient von Spearman.- 3.7 Regressionsanalyse.- 3.7.1 Einführung.- 3.7.2 Einfache lineare Regression.- 3.7.2.1 Punktschätzung der Modellparameter.- 3.7.2.2 Konfidenz- und Prognoseintervalle.- 3.7.2.3 Tests über Regressionskoeffizienten und Anpassung.- 3.7.3 Nichtlineare Regressionsfunktion.- 3.7.3.1 Polynomiale Regressionsfunktion.- 3.7.3.2 Exponentielle Regressionsfunktion.- 3.7.4 Mehrfache lineare Regression.- 3.7.4.1 Punktschätzung der Modellparameter.- 3.7.4.2 Tests über Modellparameter.- 3.7.4.3 Konfidenz- und Prognoseintervalle.- 3.7.4.4 Abhängigkeits- und Prognosemaße.- 3.7.4.5 Voraussetzungen und funktionell richtiger Ansatz.- 3.7.4.6 Multikollinearität.- 3.7.4.7 Dominante Beobachtungen, Ausreißer, robuste Regression.- 3.7.4.8 Auswahl der Einflussgrößen.- 3.8 Multivariate Analyseverfahren.- 3.8.1 Grundbegriffe.- 3.8.2 Multivariate Varianzanalyse.- 3.8.2.1 Tests über Vektoren von Erwartungswerten.- 3.8.2.2 Das multivariate lineare Modell.- 3.8.2.3 Tests über Varianzstrukturen.- 3.8.3 Hauptkomponenten- und Faktoranalyse.- 3.8.3.1 Hauptkomponentenanalysen.- 3.8.3.2 Faktoranalyse.- 3.8.4 Diskrimination und Klassifikation.- 3.8.5 Clusteranalyse.- 3.8.5.1 Punktwolken und Distanzwahl.- 3.8.5.2 Zielfunktionen und Verfahrenstypen.- 3.8.5.3 Dendrogramme.- 3.8.6 Multidimensionale Skalierung.- 3.9 Statistische Versuchsplanung.- 3.9.1 Einführung.- 3.9.2 Optimale Versuchspläne.- 3.9.3 Faktorielle Versuchspläne.- 3.9.3.1 Grundlagen.- 3.9.3.2 Vollständige zweistufige faktorielle Versuchspläne.- 3.9.3.3 Teilweise zweistufige faktorielle Versuchspläne.- 3.9.3.4 Blockbildung in faktoriellen Versuchsplänen.- 3.9.3.5 Ergebnisverbesserung vermittels der Methode von Box-Wilson.- 3.10 Statistische Methoden in der Prozesskontrolle.- 3.10.1 Grundlagen.- 3.10.2 Shewart-Kontrollkarten.- 3.10.2.1 ?X- und R- Kontrollkarten.- 3.10.2.2 Kontrollkarten für Einzelmessungen.- 3.10.2.3 Kontrollkarten für die Gut-Schlecht-Prüfung.- 3.10.3 CUSUM-Kontrollkarten.- 3.10.4 EWMA-Kontrollkarten.- Tafeln.- Tafel I Verteilungsfunktion der standardisierten Normalverteilung.- Tafel III Quantile der Chi-Quadrat-Verteilung.- Tafel V Quantile der Testfunktion für den Kolmogorov-Smirnov-Test.- Tafel VIa Kritische Werte für den Zwei-Stichproben-Rang-Test von Wilcoxon (? = 0,01).- Tafel VIb Kritische Werte für den Zwei-Stichproben-Rang-Test von Wilcoxon (? = 0,05).- Tafel VII Kritische Werte für den Zwei-Stichproben-Iterationstest.- Tafel VIII Faktoren für die Konstruktion von Kontrollkarten.- Tafel IX Diskrete Wahrscheinlichkeitsverteilungen.- Tafel X Stetige Wahrscheinlichkeitsverteilungen.- Tafel XI Konfidenzintervalle.- Tafel XII Parametertests.- Literatur.

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Book SynopsisBased on well-known lectures given at Scuola Normale Superiore in Pisa, this book introduces analysis in a separable Hilbert space of infinite dimension. It starts from the definition of Gaussian measures in Hilbert spaces, concepts such as the Cameron-Martin formula, Brownian motion and Wiener integral are introduced in a simple way. These concepts are then used to illustrate basic stochastic dynamical systems and Markov semi-groups, paying attention to their long-time behavior.Trade ReviewFrom the reviews: "This is an extended version of the author’s ‘An introduction to infinite-dimensional analysis’ published by Scuola Normale Superiore, Pisa … . A well written textbook (even an introductory research monograph), suitable for teaching a graduate course." (Neils Jacob, Zentralblatt MATH, Vol. 1109 (11), 2007) "The present volume collects together … the notes of the course on infinite-dimensional analysis held by the author at the Scuola Normale Superiore of Pisa in recent years. The book is intended for people who have some knowledge of functional analysis … . It provides an extremely useful tool for those scholars who are interested in learning some basics about Gaussian measures in Hilbert spaces, Brownian motion, Markov transition semigroups … . The book is well written and all arguments are clearly and rigorously presented." (Sandra Cerrai, Mathematical Reviews, Issue 2009 a)Table of ContentsGaussian measures in Hilbert spaces.- The Cameron–Martin formula.- Brownian motion.- Stochastic perturbations of a dynamical system.- Invariant measures for Markov semigroups.- Weak convergence of measures.- Existence and uniqueness of invariant measures.- Examples of Markov semigroups.- L2 spaces with respect to a Gaussian measure.- Sobolev spaces for a Gaussian measure.- Gradient systems.

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Book SynopsisThis is the first textbook to include the matrix continued-fraction method, which is very effective in dealing with simple Fokker-Planck equations having two variables. Other methods covered are the simulation method, the eigen-function expansion, numerical integration, and the variational method. Each solution is applied to the statistics of a simple laser model and to Brownian motion in potentials. The whole is rounded off with a supplement containing a short review of new material together with some recent references. This new study edition will prove to be very useful for graduate students in physics, chemical physics, and electrical engineering, as well as for research workers in these fields.Table of Contents1. Introduction.- 1.1 Brownian Motion.- 1.1.1 Deterministic Differential Equation.- 1.1.2 Stochastic Differential Equation.- 1.1.3 Equation of Motion for the Distribution Function.- 1.2 Fokker-Planck Equation.- 1.2.1 Fokker-Planck Equation for One Variable.- 1.2.2 Fokker-Planck Equation for N Variables.- 1.2.3 How Does a Fokker-Planck Equation Arise?.- 1.2.4 Purpose of the Fokker-Planck Equation.- 1.2.5 Solutions of the Fokker-Planck Equation.- 1.2.6 Kramers and Smoluchowski Equations.- 1.2.7 Generalizations of the Fokker-Planck Equation.- 1.3 Boltzmann Equation.- 1.4 Master Equation.- 2. Probability Theory.- 2.1 Random Variable and Probability Density.- 2.2 Characteristic Function and Cumulants.- 2.3 Generalization to Several Random Variables.- 2.3.1 Conditional Probability Density.- 2.3.2 Cross Correlation.- 2.3.3 Gaussian Distribution.- 2.4 Time-Dependent Random Variables.- 2.4.1 Classification of Stochastic Processes.- 2.4.2 Chapman-Kolmogorov Equation.- 2.4.3 Wiener-Khintchine Theorem.- 2.5 Several Time-Dependent Random Variables.- 3. Langevin Equations.- 3.1 Langevin Equation for Brownian Motion.- 3.1.1 Mean-Squared Displacement.- 3.1.2 Three-Dimensional Case.- 3.1.3 Calculation of the Stationary Velocity Distribution Function.- 3.2 Ornstein-Uhlenbeck Process.- 3.2.1 Calculation of Moments.- 3.2.2 Correlation Function.- 3.2.3 Solution by Fourier Transformation.- 3.3 Nonlinear Langevin Equation, One Variable.- 3.3.1 Example.- 3.3.2 Kramers-Moyal Expansion Coefficients.- 3.3.3 Itô’s and Stratonovich’s Definitions.- 3.4 Nonlinear Langevin Equations, Several Variables.- 3.4.1 Determination of the Langevin Equation from Drift and Diffusion Coefficients.- 3.4.2 Transformation of Variables.- 3.4.3 How to Obtain Drift and Diffusion Coefficients for Systems.- 3.5 Markov Property.- 3.6 Solutions of the Langevin Equation by Computer Simulation.- 4. Fokker-Planck Equation.- 4.1 Kramers-Moyal Forward Expansion.- 4.1.1 Formal Solution.- 4.2 Kramers-Moyal Backward Expansion.- 4.2.1 Formal Solution.- 4.2.2 Equivalence of the Solutions of the Forward and Backward Equations.- 4.3 Pawula Theorem.- 4.4 Fokker-Planck Equation for One Variable.- 4.4.1 Transition Probability Density for Small Times.- 4.4.2 Path Integral Solutions.- 4.5 Generation and Recombination Processes.- 4.6 Application of Truncated Kramers-Moyal Expansions.- 4.7 Fokker-Planck Equation for N Variables.- 4.7.1 Probability Current.- 4.7.2 Joint Probability Distribution.- 4.7.3 Transition Probability Density for Small Times.- 4.8 Examples for Fokker-Planck Equations with Several Variables.- 4.8.1 Three-Dimensional Brownian Motion without Position Variable.- 4.8.2 One-Dimensional Brownian Motion in a Potential.- 4.8.3 Three-Dimensional Brownian Motion in an External Force.- 4.8.4 Brownian Motion of Two Interacting Particles in an External Potential.- 4.9 Transformation of Variables.- 4.10 Covariant Form of the Fokker-Planck Equation.- 5. Fokker-Planck Equation for One Variable; Methods of Solution.- 5.1 Normalization.- 5.2 Stationary Solution.- 5.3 Ornstein-Uhlenbeck Process.- 5.4 Eigenfunction Expansion.- 5.5 Examples.- 5.5.1 Parabolic Potential.- 5.5.2 Inverted Parabolic Potential.- 5.5.3 Infinite Square Well for the Schrüdinger Potential.- 5.5.4 V-Shaped Potential for the Fokker-Planck Equation.- 5.6 Jump Conditions.- 5.7 A Bistable Model Potential.- 5.8 Eigenfunctions and Eigenvalues of Inverted Potentials.- 5.9 Approximate and Numerical Methods for Determining Eigenvalues and Eigenfunctions.- 5.9.1 Variational Method.- 5.9.2 Numerical Integration.- 5.9.3 Expansion into a Complete Set.- 5.10 Diffusion Over a Barrier.- 5.10.1 Kramers’ Escape Rate.- 5.10.2 Bistable and Metastable Potential.- 6. Fokker-Planck Equation for Several Variables; Methods of Solution.- 6.1 Approach of the Solutions to a Limit Solution.- 6.2 Expansion into a Biorthogonal Set.- 6.3 Transformation of the Fokker-Planck Operator, Eigenfunction Expansions.- 6.4 Detailed Balance.- 6.5 Ornstein-Uhlenbeck Process.- 6.6 Further Methods for Solving the Fokker-Planck Equation.- 6.6.1 Transformation of Variables.- 6.6.2 Variational Method.- 6.6.3 Reduction to an Hermitian Problem.- 6.6.4 Numerical Integration.- 6.6.5 Expansion into Complete Sets.- 6.6.6 Matrix Continued-Fraction Method.- 6.6.7 WKB Method.- 7. Linear Response and Correlation Functions.- 7.1 Linear Response Function.- 7.2 Correlation Functions.- 7.3 Susceptibility.- 8. Reduction of the Number of Variables.- 8.1 First-Passage Time Problems.- 8.2 Drift and Diffusion Coefficients Independent of Some Variables.- 8.2.1 Time Integrals of Markovian Variables.- 8.3 Adiabatic Elimination of Fast Variables.- 8.3.1 Linear Process with Respect to the Fast Variable.- 8.3.2 Connection to the Nakajima-Zwanzig Projector Formalism.- 9. Solutions of Tridiagonal Recurrence Relations, Application to Ordinary and Partial Differential Equations.- 9.1 Applications and Forms of Tridiagonal Recurrence Relations.- 9.1.1 Scalar Recurrence Relation.- 9.1.2 Vector Recurrence Relation.- 9.2 Solutions of Scalar Recurrence Relations.- 9.2.1 Stationary Solution.- 9.2.2 Initial Value Problem.- 9.2.3 Eigenvalue Problem.- 9.3 Solutions of Vector Recurrence Relations.- 9.3.1 Initial Value Problem.- 9.3.2 Eigenvalue Problem.- 9.4 Ordinary and Partial Differential Equations with Multiplicative Harmonic Time-Dependent Parameters.- 9.4.1 Ordinary Differential Equations.- 9.4.2 Partial Differential Equations.- 9.5 Methods for Calculating Continued Fractions.- 9.5.1 Ordinary Continued Fractions.- 9.5.2 Matrix Continued Fractions.- 10. Solutions of the Kramers Equation.- 10.1 Forms of the Kramers Equation.- 10.1.1 Normalization of Variables.- 10.1.2 Reversible and Irreversible Operators.- 10.1.3 Transformation of the Operators.- 10.1.4 Expansion into Hermite Functions.- 10.2 Solutions for a Linear Force.- 10.2.1 Transition Probability.- 10.2.2 Eigenvalues and Eigenfunctions.- 10.3 Matrix Continued-Fraction Solutions of the Kramers Equation.- 10.3.1 Initial Value Problem.- 10.3.2 Eigenvalue Problem.- 10.4 Inverse Friction Expansion.- 10.4.1 Inverse Friction Expansion for K0(t), G0,0(t) and L0(t).- 10.4.2 Determination of Eigenvalues and Eigenvectors.- 10.4.3 Expansion for the Green’s Function Gn,m(t).- 10.4.4 Position-Dependent Friction.- 11. Brownian Motion in Periodic Potentials.- 11.1 Applications.- 11.1.1 Pendulum.- 11.1.2 Superionic Conductor.- 11.1.3 Josephson Tunneling Junction.- 11.1.4 Rotation of Dipoles in a Constant Field.- 11.1.5 Phase-Locked Loop.- 11.1.6 Connection to the Sine-Gordon Equation.- 11.2 Normalization of the Langevin and Fokker-Planck Equations.- 11.3 High-Friction Limit.- 11.3.1 Stationary Solution.- 11.3.2 Time-Dependent Solution.- 11.4 Low-Friction Limit.- 11.4.1 Transformation to E and x Variables.- 11.4.2 ‘Ansatz’ for the Stationary Distribution Functions.- 11.4.3 x-Independent Functions.- 11.4.4 x-Dependent Functions.- 11.4.5 Corrected x-Independent Functions and Mobility.- 11.5 Stationary Solutions for Arbitrary Friction.- 11.5.1 Periodicity of the Stationary Distribution Function.- 11.5.2 Matrix Continued-Fraction Method.- 11.5.3 Calculation of the Stationary Distribution Function.- 11.5.4 Alternative Matrix Continued Fraction for the Cosine Potential.- 11.6 Bistability between Running and Locked Solution.- 11.6.1 Solutions Without Noise.- 11.6.2 Solutions With Noise.- 11.6.3 Low-Friction Mobility With Noise.- 11.7 Instationary Solutions.- 11.7.1 Diffusion Constant.- 11.7.2 Transition Probability for Large Times.- 11.8 Susceptibilities.- 11.8.1 Zero-Friction Limit.- 11.9 Eigenvalues and Eigenfunctions.- 11.9.1 Eigenvalues and Eigenfunctions in the Low-Friction Limit.- 12. Statistical Properties of Laser Light.- 12.1 Semiclassical Laser Equations.- 12.1.1 Equations Without Noise.- 12.1.2 Langevin Equation.- 12.1.3 Laser Fokker-Planck Equation.- 12.2 Stationary Solution and Its Expectation Values.- 12.3 Expansion in Eigenmodes.- 12.4 Expansion into a Complete Set; Solution by Matrix Continued Fractions.- 12.4.1 Determination of Eigenvalues.- 12.5 Transient Solution.- 12.5.1 Eigenfunction Method.- 12.5.2 Expansion into a Complete Set.- 12.5.3 Solution for Large Pump Parameters.- 12.6 Photoelectron Counting Distribution.- 12.6.1 Counting Distribution for Short Intervals.- 12.6.2 Expectation Values for Arbitrary Intervals.- Appendices.- A1 Stochastic Differential Equations with Colored Gaussian Noise.- A2 Boltzmann Equation with BGK and SW Collision Operators.- A3 Evaluation of a Matrix Continued Fraction for the Harmonic Oscillator.- A4 Damped Quantum-Mechanical Harmonic Oscillator.- A5 Alternative Derivation of the Fokker-Planck Equation.- A6 Fluctuating Control Parameter.- S. Supplement to the Second Edition.- S.1 Solutions of the Fokker-Planck Equation by Computer Simulation (Sect. 3.6).- S.2 Kramers-Moyal Expansion (Sect. 4.6).- S.3 Example for the Covariant Form of the Fokker-Planck Equation (Sect. 4.10).- S.4 Connection to Supersymmetry and Exact Solutions of the One Variable Fokker-Planck Equation (Chap. 5).- S.5 Nondifferentiability of the Potential for the Weak Noise Expansion (Sects. 6.6 and 6.7).- S.6 Further Applications of Matrix Continued-Fractions (Chap. 9).- S.7 Brownian Motion in a Double-Well Potential (Chaps. 10 and 11).- S.8 Boundary Layer Theory (Sect. 11.4).- S.9 Calculation of Correlation Times (Sect. 7.12).- S.10 Colored Noise (Appendix A1).- S.11 Fokker-Planck Equation with a Non-Positive-Definite Diffusion Matrix and Fokker-Planck Equation with Additional Third-Order-Derivative Terms.- References.

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Book Synopsisto the Statistical Analysis of Categorical Data With 16 Figures and 121 Tables , Springer Prof. Erling B. Andersen University of Copenhagen Department of Statistics 6 Studiestrrede DK-14SS Copenhagen Denmark ISBN 978-3-540-62399-1 CataJoging-in-Publication Data applied ror Oie Oeutsche Bibliothek - CIP-Einheitsaufnahme Andersen, Erling B. : Introduction to the statistical analysis of categorical data analysis: with 121 tables I Erling B. Andersen. -Berlin; Heidelberg; New York; Bucelona; Buda- pest; Hong Kong; London; Milan; Paris; Santa Clara; Singapore; Tokyo: Springer, 1997 ISBN 978-3-540~2399-1 ISBN 978-3~2-59123-5 (eBook) DOI10. 1007/978-3~2-59123-5 This work is subject to copyright. AII rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any o!her way, and storage in data banks. Ouplication of this publication or parts thereof IS permitted only under the provisions of !he German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under !he German Copyright Law. O Springer-Verlag Berlin Heidelberg 1997 Originally published by Springer-Verlag Berlin Heidelberg New York in 1997 The use of general descriptive names, registered names, trademarks, etc. in this publi- cation does not imply, even in !he absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and !herefore free for general use.Table of ContentsIntroduction: The two-way Table.- Basic Theory: Exponential families; Statistical inference in an exponential family; The binomial distribution; The Poisson distribution; Composite hypotheses; Applications to the multinomial distribution; Log-linear models; The two-way contingency table; The numerical solution of the likelihood equations for the log-linear model.- Three-way contingency tables: Log-linear models; Log-linear hypotheses; Estimation; testing hypotheses; Interpretation of the log-linear parameters; Choice of model; Detection of model deviations.- Multi-dimensional contingency tables: The log-linear-model; Classification and interpretation of log-linear models; Choice of model; Diagnostics; Model search strategies.- Incomplete Tables: Random and structural zeros; Counting th number of degrees of freedom; Validity of the X2-approximation.- The Logit Model: The Logit model; Hypothesis testing in the logit model; Logit models with higher order interactions; The Logit model as a regression model.- Logistic Regression Analysis: The logistic regression model; Estimation in the logistic regression model; Numerical solution of the likelihood equations; Checking the fit of the model; Hypothesis testing; Diagnostics; Predictions; Dummy variables; Polytomous response variables.- Association Models: Symmetry models; Marginal homogeneity; RC-association models; Correspondence analysis.- Appendix: Solutions and output to selected excercises.

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Book SynopsisThis richly illustrated book is an exploration of how chance and risk, on the one hand, and meaning or significance on the other, compete for the limelight in art, in philosophy, and in science. In modern society, prudence and probability calculation permeate our daily lives. Yet it is clear for all to see that neither cautious bank regulations nor mathematics have prevented economic crises from occurring time and again. Nicolas Bouleau argues that it is the meaning we assign to an event that determines the perceived risk, and that we generally turn a blind eye to this important fact, because the word "meaning" is itself awkward to explain. He tackles this fundamental question through examples taken from cultural fields ranging from painting, architecture, and music, to poetry, biology, and astronomy. This enables the reader to view overwhelming risks in a different light. Bouleau clarifies that the most important thing in a time of uncertainty is to think of prudence on a higher level, one that truly addresses the various subjective interpretations of the world.Trade ReviewFrom the reviews:“Risk and Meaning is a quirky book, both in its topic and its physical layout and the writing style that the author adopts … . it is a book that is mathematics-adjacent, and I think that anyone who is able to get past (or even relish) some of the quirkiness in the book will certainly find themselves with plenty to think about. And I’m not sure what more one could ask for in a book.” (Darren Glass, The Mathematical Association of America, November, 2011)Table of ContentsEntrance: Interpretation and Paradigms.- I. Cicero and Divination.- II. Cournot’s "Philosophic Probabilities".- III. Mathematical Probabilities.- IV. Democracy by Chance.- V. Gestalt, Structure, Pattern.- VI. The Third Dimension of Risk.- VII. ''Modern" Architecture.- VIII. The Ideal City.- IX. Daring the Abstract in Art.- X. Saussure or the Dread of Mathematical Probabilities.- XI. Jacques Monod’s Roulette.- XII. From Fortuitism to Animism.- XIII. The Slip as Fortuity and Meaning.- XIV. Guessing Astronomy.- XV. The Legitimacy of Science and Love.- Hints and Index.

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Book SynopsisFrom the Preface: Srinivasa Varadhan began his research career at the Indian Statistical Institute (ISI), Calcutta, where he started as a graduate student in 1959. His first paper appeared in Sankhyá, the Indian Journal of Statistics in 1962. Together with his fellow students V. S. Varadarajan, R. Ranga Rao and K. R. Parthasarathy, Varadhan began the study of probability on topological groups and on Hilbert spaces, and quickly gained an international reputation. At this time Varadhan realised that there are strong connections between Markov processes and differential equations, and in 1963 he came to the Courant Institute in New York, where he has stayed ever since. Here he began working with the probabilists Monroe Donsker and Marc Kac, and a graduate student named Daniel Stroock. He wrote a series of papers on the Martingale Problem and Diffusions together with Stroock, and another series of papers on Large Deviations together with Donsker. With this work Varadhan's reputation as one of the leading mathematicians of the time was firmly established. Since then he has contributed to several other areas of probability, analysis and physics, and collaborated with numerous distinguished mathematicians. Varadhan was awarded the Abel Prize in 2007. These Collected Works contain all his research papers over the half-century spanning 1962 to early 2012. Volume I includes the introductory material, the papers on limit theorems and review articles.Table of ContentsAutobiography: S. R. S. Varadhan.- Introduction: S. R. S. Varadhan.- Prize Citations.- Diffusion Theory by Daniel W. Stroock. - Large Deviations by Daniel W. Stroock.- Large Deviation and Homogenization by Fraydoun Rezakhanlou.- Varadhan's Work on Hydrodynamical Limits by Jeremy Quastel and Horng-Tzer Yau.- Book Review: Multidimensional Diffusion Processes by D. W. Stroock and S. R. S. Varadhan.- Limit Theorems: Limit theorems for sums of independent random variables with values in a Hilbert space.- On the category of indecomposable distributions on topological groups.- Probability distributions on locally compact abelian groups.- Extension of stationary stochastic processes.- Limit theorems in probability.- A limit theorem with strong mixing in Banach space and two applications to stochastic differential equations.- Limit theorems for random walks on Lie groups.- Martingale approach to some limit theorems.- Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions.- Bounding functions of Markov processes and the shortest queue problem.- Finite approximations to quantum systems.- Self-diffusion of a tagged particle in equilibrium for asymmetric mean zero random walk with simple exclusion.- Diffusive limit of a tagged particle in asymmetric simple exclusion processes.- A martingale proof of Dobrushin's theorem for non-homogeneous Markov chains.- Review Articles.- Diffusion processes, Stochastic processes: theory and methods.- Stochastic analysis and applications.- Large deviations and entropy, Entropy.- The role of weak convergence in probability theory.

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Book SynopsisFrom the Preface: Srinivasa Varadhan began his research career at the Indian Statistical Institute (ISI), Calcutta, where he started as a graduate student in 1959. His first paper appeared in Sankhyá, the Indian Journal of Statistics in 1962. Together with his fellow students V. S. Varadarajan, R. Ranga Rao and K. R. Parthasarathy, Varadhan began the study of probability on topological groups and on Hilbert spaces, and quickly gained an international reputation. At this time Varadhan realised that there are strong connections between Markov processes and differential equations, and in 1963 he came to the Courant Institute in New York, where he has stayed ever since. Here he began working with the probabilists Monroe Donsker and Marc Kac, and a graduate student named Daniel Stroock. He wrote a series of papers on the Martingale Problem and Diffusions together with Stroock, and another series of papers on Large Deviations together with Donsker. With this work Varadhan's reputation as one of the leading mathematicians of the time was firmly established. Since then he has contributed to several other areas of probability, analysis and physics, and collaborated with numerous distinguished mathematicians. Varadhan was awarded the Abel Prize in 2007. These Collected Works contain all his research papers over the half-century spanning 1962 to early 2012. Volume II includes the papers on PDE, SDE, diffusions, and random media.​​Table of ContentsVol. II: Diffusion processes with continuous coefficients - I (with D. W. Stroock).- Diffusion processes with continuous coefficients - II (with D. W. Stroock).- Diffusion processes with boundary conditions (with D. W. Stroock).- On degenerate elliptic-parabolic operators of second order and their associated diffusions (with D. W. Stroock).- On the support of diffusion processes with applications to the strong maximum principle (with D. W. Stroock).- Diffusion processes (with D. W. Stroock).- A probabilistic approach to Hp(Rd) (with D. W. Stroock).- Kac functional and Schrodinger equation (with K. L. Chung).- Brownian motion in a wedge with oblique reection (with R. J. Williams).- A multidimensional process involving local time (with A.S. Sznitman).- Etat fondamental et principe du maximum pour les operateurs elliptiques du second ordre dans des domaines generaux. [The ground state and maximum principle for second-order elliptic operators in general domains] (with H. Berestycki and L. Nirenberg).- The principal eigenvalue and maximum principle for second-order elliptic operators in general domains (with H. Berestycki and L. Nirenberg).- Diffusion semigroups and di_usion processes corresponding to degenerate divergence form operators (with J. Quastel).- Random Media.- Diffusion in regions with many small holes (with G. Papanicolaou).- Boundary value problems with rapidly oscillating random coefficients (with G. Papanicolaou).- Diffusions with random coefficients (with G. Papanicolaou).- Ohrnstein-Uhlenbeck process in a random potential (with G. Papanicolaou).- Large deviations for random walks in a random environment.- Random walks in a random environment.- Stochastic homogenization of Hamilton-Jacobi-Bellman equations (with E. Kosygina and F. Rezakhanlou).- Homogenization of Hamilton-Jacobi-Bellman equations with respect to time-space shifts in a stationary ergodic medium (with E. Kosygina).- Behavior of the solution of a random semilinear heat equation (with N. Zygouras).​

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Book SynopsisFrom the Preface: Srinivasa Varadhan began his research career at the Indian Statistical Institute (ISI), Calcutta, where he started as a graduate student in 1959. His first paper appeared in Sankhyá, the Indian Journal of Statistics in 1962. Together with his fellow students V. S. Varadarajan, R. Ranga Rao and K. R. Parthasarathy, Varadhan began the study of probability on topological groups and on Hilbert spaces, and quickly gained an international reputation. At this time Varadhan realised that there are strong connections between Markov processes and differential equations, and in 1963 he came to the Courant Institute in New York, where he has stayed ever since. Here he began working with the probabilists Monroe Donsker and Marc Kac, and a graduate student named Daniel Stroock. He wrote a series of papers on the Martingale Problem and Diffusions together with Stroock, and another series of papers on Large Deviations together with Donsker. With this work Varadhan's reputation as one of the leading mathematicians of the time was firmly established. Since then he has contributed to several other areas of probability, analysis and physics, and collaborated with numerous distinguished mathematicians. Varadhan was awarded the Abel Prize in 2007. These Collected Works contain all his research papers over the half-century spanning 1962 to early 2012.Volume III includes the papers on large deviations. ​​Table of ContentsLarge Deviations.- Asymptotic probabilities and differential equations.- On the behavior of the fundamental solution of the heat equation with variable coefficients .- Diffusion processes in a small time interval .- On a variational formula for the principal eigenvalue for operators with maximum principle.- Asymptotic evaluation of certain Markov process expectations for large time I.- Asymptotic evaluation of certain Markov process expectations for large time II.- Asymptotic evaluation of certain Wiener integrals for large time.- Asymptotics for the Wiener sausage.- Erratum: Asymptotics for the Wiener sausage.- Asymptotic evaluation of certain Markov process expectations for large time III.- On the principal eigenvalue of second-order elliptic differential operators.- On laws of the iterated logarithm for local times.- Some problems of large deviations.- On the number of distinct sites visited by a random walk.- A law of the iterated logarithm for total occupation times of transient Brownian motion.- Some problems of large deviations .- The polaron problem and large deviations.- Asymptotic evaluation of certain Markov process expectations for large time IV.- Asymptotics for the polaron.- Large deviations for stationary Gaussian processes.- Large deviations and applications.- Large deviations for non-interacting infinite-particle systems.- Some familiar examples for which the large deviation principle does not hold.- The large deviation principle for the Erdös-Rényi random graph.- Large deviations for random matrices. ​

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Book SynopsisFrom the Preface: Srinivasa Varadhan began his research career at the Indian Statistical Institute (ISI), Calcutta, where he started as a graduate student in 1959. His first paper appeared in Sankhyá, the Indian Journal of Statistics in 1962. Together with his fellow students V. S. Varadarajan, R. Ranga Rao and K. R. Parthasarathy, Varadhan began the study of probability on topological groups and on Hilbert spaces, and quickly gained an international reputation. At this time Varadhan realised that there are strong connections between Markov processes and differential equations, and in 1963 he came to the Courant Institute in New York, where he has stayed ever since. Here he began working with the probabilists Monroe Donsker and Marc Kac, and a graduate student named Daniel Stroock. He wrote a series of papers on the Martingale Problem and Diffusions together with Stroock, and another series of papers on Large Deviations together with Donsker. With this work Varadhan's reputation as one of the leading mathematicians of the time was firmly established. Since then he has contributed to several other areas of probability, analysis and physics, and collaborated with numerous distinguished mathematicians. Varadhan was awarded the Abel Prize in 2007. These Collected Works contain all his research papers over the half-century spanning 1962 to early 2012. Volume IV includes the papers on particle systems.Table of ContentsVolume 4: Particle Systems and Their Large Deviations.- Nonlinear diffusion limit for a system with nearest neighbor interaction.- Hydrodynamics and large deviation for simple exclusion processes.- Large deviations from a hydrodynamic scaling limit.- On the derivation of conservation laws for stochastic dynamics.- Scaling limits for interacting diffusions.- Scaling limit for interacting Ornstein-Uhlenbeck processes.- Entropy methods in hydrodynamical scaling.- Hydrodynamical limit for a Hamiltonian system with weak noise.- Nonlinear diffusion limit for a system with nearest neighbor interactions II.- Regularity of self-diffusion coefficient.- Entropy methods in hydrodynamic scaling.- Spectral gap for zero-range dynamics.- The complex story of simple exclusion.- Non-gradient models in hydrodynamic scaling.- Relative entropy and mixing properties of interacting particle systems.- Diffusive limit of lattice gas with mixing conditions.- Large deviations for the symmetric simple exclusion process in dimensions d > 3.- Large deviations for interacting particle systems.- Infinite particle systems and their scaling limits.- Lectures on hydrodynamic scaling.- Scaling limits of large interacting systems .- Asymptotic behavior of a tagged particle in simple exclusion processes.- Large deviation and hydrodynamic scaling.- Symmetric simple exclusion process: regularity of the self-diffusion coefficient.- Finite-dimensional approximation of the self-diffusion coefficient for the exclusion process.- Large deviations for the asymmetric simple exclusion process.- Diffusive behaviour of the equilibrium fluctuations in the asymmetric exclusion processes.- On viscosity and fluctuation-dissipation in exclusion processes.- Large deviations for the current and tagged particle in 1d nearest neighbor.- Symmetric simple exclusion.- List of Publications of S.R.S. Varadhan.- Acknowledgements. ​

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Book SynopsisLong-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.Trade ReviewFrom the book reviews:“This encyclopaedic book covers almost the whole literature on univariate and multivariate long-range dependent (LRD) processes, or long-memory processes or strongly dependent processes. … This volume is then of strong interest for both researchers and teachers familiar with the topic, as it gives an overall, structured and balanced picture of the current state of the art. Readers less familiar with the topic will easily find their way in the vast literature on this issue, and will have their curiosity satisfied.” (Gilles Teyssière, Mathematical Reviews, October, 2014)“This book aims to cover probabilistic and statistical aspects of long-memory processes in as much detail as possible, including a broad range of topics. The authors did an excellent job to reach their goals, and the book would be a must for researchers interested in long-memory processes and practioners on time series and data analysis. … the book is an excellent choice for anyone who is working in fields related to long-memory processes with many update information and research topics.” (Weiping Li, zbMATH, Vol. 1282, 2014)Table of ContentsDefinition of Long Memory.- Origins and Generation of Long Memory.- Mathematical Concepts.- Limit Theorems.- Statistical Inference for Stationary Processes.- Statistical Inference for Nonlinear Processes.- Statistical Inference for Nonstationary Processes.- Forecasting.- Spatial and Space-Time Processes.- Resampling.- Function Spaces.- Regularly Varying Functions.- Vague Convergence.- Some Useful Integrals.- Notation and Abbreviations.

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Book SynopsisOne-dimensional dynamics has developed in the last decades into a subject in its own right. Yet, many recent results are inaccessible and have never been brought together. For this reason, we have tried to give a unified ac count of the subject and complete proofs of many results. To show what results one might expect, the first chapter deals with the theory of circle diffeomorphisms. The remainder of the book is an attempt to develop the analogous theory in the non-invertible case, despite the intrinsic additional difficulties. In this way, we have tried to show that there is a unified theory in one-dimensional dynamics. By reading one or more of the chapters, the reader can quickly reach the frontier of research. Let us quickly summarize the book. The first chapter deals with circle diffeomorphisms and contains a complete proof of the theorem on the smooth linearizability of circle diffeomorphisms due to M. Herman, J.-C. Yoccoz and others. Chapter II treats the kneading theory of Milnor and Thurstonj also included are an exposition on Hofbauer's tower construction and a result on fuB multimodal families (this last result solves a question posed by J. Milnor).Table of Contents0. Introduction.- I. Circle Diffeomorphisms.- 1. The Combinatorial Theory of Poincaré.- 2. The Topological Theory of Denjoy.- 2.a The Denjoy Inequality.- 2.b Ergodicity.- 3. Smooth Conjugacy Results.- 4. Families of Circle Diffeomorphisms; Arnol’d tongues.- 5. Counter-Examples to Smooth Linearizability.- 6. Frequency of Smooth Linearizability in Families.- 7. Some Historical Comments and Further Remarks.- II. The Combinatorics of One-Dimensional Endomorphisms.- 1. The Theorem of Sarkovskii.- 2. Covering Maps of the Circle as Dynamical Systems.- 3. The Kneading Theory and Combinatorial Equivalence.- 3.a Examples.- 3.b Hofbauer’s Tower Construction.- 4. Full Families and Realization of Maps.- 5. Families of Maps and Renormalization.- 6. Piecewise Monotone Maps can be Modelled by Polynomial Maps.- 7. The Topological Entropy.- 8. The Piecewise Linear Model.- 9. Continuity of the Topological Entropy.- 10. Monotonicity of the Kneading Invariant for the Quadratic Family.- 11. Some Historical Comments and Further Remarks.- III. Structural Stability and Hyperbolicity.- 1. The Dynamics of Rational Mappings.- 2. Structural Stability and Hyperbolicity.- 3. Hyperbolicity in Maps with Negative Schwarzian Derivative.- 4. The Structure of the Non-Wandering Set.- 5. Hyperbolicity in Smooth Maps.- 6. Misiurewicz Maps are Almost Hyperbolic.- 7. Some Further Remarks and Open Questions.- IV. The Structure of Smooth Maps.- 1. The Cross-Ratio: the Minimum and Koebe Principle.- l.a Some Facts about the Schwarzian Derivative.- 2. Distortion of Cross-Ratios.- 2.a The Zygmund Conditions.- 3. Koebe Principles on Iterates.- 4. Some Simplifications and the Induction Assumption.- 5. The Pullback of Space: the Koebe/Contraction Principle.- 6. Disjointness of Orbits of Intervals.- 7. Wandering Intervals Accumulate on Turning Points.- 8. Topological Properties of a Unimodal Pullback.- 9. The Non-Existence of Wandering Intervals.- 10. Finiteness of Attractors.- 11. Some Further Remarks and Open Questions.- V. Ergodic Properties and Invariant Measures.- 1. Ergodicity, Attractors and Bowen-Ruelle-Sinai Measures.- 2. Invariant Measures for Markov Maps.- 3. Constructing Invariant Measures by Inducing.- 4. Constructing Invariant Measures by Pulling Back.- 5. Transitive Maps Without Finite Continuous Measures.- 6. Frequency of Maps with Positive Liapounov Exponents in Families and Jakobson’s Theorem.- 7. Some Further Remarks and Open Questions.- VI. Renormalization.- 1. The Renormalization Operator.- 2. The Real Bounds.- 3. Bounded Geometry.- 4. The PullBack Argument.- 5. The Complex Bounds.- 6. Riemann Surface Laminations.- 7. The Almost Geodesic Principle.- 8. Renormalization is Contracting.- 9. Universality of the Attracting Cantor Set.- 10. Some Further Remarks and Open Questions.- VII. Appendix.- 1. Some Terminology in Dynamical Systems.- 2. Some Background in Topology.- 3. Some Results from Analysis and Measure Theory.- 4. Some Results from Ergodic Theory.- 5. Some Background in Complex Analysis.- 6. Some Results from Functional Analysis.

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Book SynopsisIn chapter 1, the basic assumptions of the random vibration theory are emphasized. In chapters 2 and 3, pertinent results of stochastic variables and stochastic processes have been indicated. Chapter 4 deals with the stochastic response analysis of single degrees-of-freedom, multi-degrees-of-freedom and continuous linear structural systems. In principle, an introductory course on linear structural dynamics is presupposes. However, in order to make this textbook self-contained, short reviews of the most important results of linear deterministic vibration theory have been included in the start of the relevant sub-sections. Chapter 5 outlines the reliability theory for dynamically excited building structures, i.e., reliability theory for narrowbanded response processes. Finally, Chapter 6 gives an introduction to Monte Carlo simulation methods, which become increasingly important and useful as the computers become more and more powerful.

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Book SynopsisThe notes are based on lectures on stochastic processes given at Scuola Normale Superiore in 1999 and 2000. Some new material was added and only selected, less standard results were presented. We did not include several applications to statistical mechanics and mathematical finance, covered in the lectures, as we hope to write part two of the notes devoted to applications of stochastic processes in modelling. The main themes of the notes are constructions of stochastic processes. We present different approaches to the existence question proposed by Kolmogorov, Wiener, Ito and Prohorov. Special attention is also paid to Levy processes. The lectures are basically self-contained and rely only on elementary measure theory and functional analysis. They might be used for more advanced courses on stochastic processes.

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Book SynopsisThe lectures concentrate on some old and new relations between quasiderivatives of solutions to Ito stochastic equations and interior smoothness of harmonic functions associated with degenerate elliptic equations. Recent progress in the case of constant coefficients is discussed in full detail.

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Book SynopsisThis volume explores, from a mathematical and a philosophical perspective, the virtuous circle connecting logic and rationality. While logic lends its methods, techniques and ideas to the investigation of rationality, the practical problems which arise in modelling rational behaviour, especially in the social sciences, motivate logicians to develop more refined logical formalisms. This is why non classical logics - a unifying theme of this volume - play a fundamental role in the construction of formal models of rationality.Table of ContentsIntroduction.- 1. Foundations.- Ordered algebras and logic. George Metcalfe, Francesco Paoli and Constantine Tsinakis.- 2. Probability. The social entropy process: Axiomatising the aggregation of probabilistic beliefs. George Wilmers.- Conditional probability in the light of qualitative belief change. David Makinson.- Is there a probability theory of many-valued events? Vincenzo Marra.- 3. Uncertainty.- On Giles style dialogue games and hypersequent systems. Christian G. Fermüller.- Poset representation for free RDP-algebras. Diego Valota.- Uncertainty, indeterminacy and fuzziness: A probabilistic approach. Martina Fedel.- 4. Rationality.- Tractable depth-bounded logics and the problem of logical omniscience. Marcello D’Agostino.- Rational behaviour at trust nodes. Hykel Hosni and Silvia Milano.

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Book SynopsisThis is the first book of two volumes covering the basics of statistical methods and analysis. Significant topics include concepts of research and data analysis, descriptive statistics, probability and distributions, correlation and regression, and statistical inference. The book includes useful examples and exercises as well as relevant case studies for proper implementation of the discussed tools. This book will be a valuable text for undergraduate students of statistics, management, economics, and psychology, wanting to gain basic understanding of statistics and the usage of its various concepts. Table of Contents1. Concepts in Research and Data Analysis.- 2. Descriptive Statistics.- 3. Probability and Distributions.- 4. Correlation and Regression.- 5. Statistical Inference.

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Book SynopsisThis book presents the fundamentals of stochastic thermodynamics, one of the most central subjects in non-equilibrium statistical mechanics. It also explores many recent advances, e.g., in information thermodynamics, the thermodynamic uncertainty relation, and the trade-off relation between efficiency and power. The content is divided into three main parts, the first of which introduces readers to fundamental topics in stochastic thermodynamics, e.g., the basics of stochastic processes, the fluctuation theorem and its variants, information thermodynamics, and large deviation theory. In turn, parts two and three explore advanced topics such as autonomous engines (engines not controlled externally) and finite speed engines, while also explaining the key concepts from recent stochastic thermodynamics theory that are involved. To fully benefit from the book, readers only need an undergraduate-level background in statistical mechanics and quantum mechanics; no background in information theory or stochastic processes is needed. Accordingly, the book offers a valuable resource for early graduate or higher-level readers who are unfamiliar with this subject but want to keep up with the cutting-edge research in this field. In addition, the author’s vivid descriptions interspersed throughout the book will help readers grasp ‘living’ research developments and begin their own research in this field.Table of Contents

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Book SynopsisThis treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.Table of Contents1 Introduction.- 2 Stochastic Volatility Model.- 3 Asymmetric Stochastic Volatility Model.- 4 Stochastic Volatility Model with Generalized Hyperbolic Skew Student’s t Error.- 5 Realized Stochastic Volatility Model.

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Book SynopsisThis book focuses on research in linear algebra, statistics, matrices, graphs and their applications. Many chapters in the book feature new findings due to applications of matrix and graph methods. The book also discusses rediscoveries of the subject by using new methods. Dedicated to Prof. Calyampudi Radhakrishna Rao (C.R. Rao) who has completed 100 years of legendary life and continues to inspire us all and Prof. Arbind K. Lal who has sadly departed us too early, it has contributions from collaborators, students, colleagues and admirers of Professors Rao and Lal. With many chapters on generalized inverses, matrix analysis, matrices and graphs, applied probability and statistics, and the history of ancient mathematics, this book offers a diverse array of mathematical results, techniques and applications. The book promises to be especially rewarding for readers with an interest in the focus areas of applied linear algebra, probability and statistics.Table of ContentsChapter 1. On Some Matrix Versions of Covariance, Harmonic Mean and other Inequalities: An Overview.- Chapter 2. The Impact of Professor C. R. Rao's Research used in solving problems in Applied Probability.- Chapter 3. Upper ounds for the Euclidean distances between the BLUEs under the partitioned linear fixed model and the corresponding mixed model.- Chapter 4. Nucleolus Computation for some Structured TU Games via Graph Theory and Linear Algebra.- Chapter 5. From Linear System of Equations to Artificial Intelligence - The evolution Journey of Computer Tomographic Image Reconstruction Algorithms.- Chapter 6. Shapley Value and other Axiomatic Extensions to Shapley Value.- Chapter 7. An Accelerated Block Randomized Kaczmarz Methos.- Chapter 8. Nullity of Graphs - A Survey and Some New Results.- Chapter 9. Some Observations on Algebraic Connectivity of Graphs.- Chapter 10. Orthogonality for iadjoints f Operators.- Chapter 11. Permissible covariance structures for simultaneous retention of BLUEs in small and big linear models.- Chapter 12. On some Special Matrices and its Applications in Linear Complementarity Problem.- Chapter 3. On Nearest Matrix with Partially Specified Eigen Structure.- Chapter 14. Equality of BLUEs for Full, Small, and Intermediate Linear Models under Covariance Change, with links to Data Confidentiality and Encryption.-Chapter 15. Statistical Inference for Middle Censored Data with Applications. etc

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Book SynopsisThis book is designed for undergraduate programs and students and can also be used as a first-year graduate text in probability. It offers a broad perspective, building on the synopsis of measure and integration offered in Chapter two.

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Book SynopsisAn Advanced Course in Probability and Stochastic Processes provides a modern and rigorous treatment of probability theory and stochastic processes at an upper undergraduate and graduate level. Starting with the foundations of measure theory, this book introduces the key concepts of probability theory in an accessible way, providing full proofs and extensive examples and illustrations. Fundamental stochastic processes such as Gaussian processes, Poisson random measures, Lévy processes, Markov processes, and Itô processes are presented and explored in considerable depth, showcasing their many interconnections. Special attention is paid to martingales and the Wiener process and their central role in the treatment of stochastic integrals and stochastic calculus. This book includes many exercises, designed to test and challenge the reader and expand their skillset. An Advanced Course in Probability and Stochastic Processes is meant for students and researchers who have a soTable of Contents1. Measure Theory 2. Probability 3. Convergence 4. Conditioning 5. Martingales 6. Wiener and Brownian Motion Processes 7. Itô Calculus Appendix A. Selected Solutions Appendix B. Function Spaces Appendix C. Existence of the Lebesgue Measure Index

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Book SynopsisSince the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, Introduction to Stochastic Calculus Applied to Finance, Second Edition incorporates some of these new techniques and concepts to provide an accessible, up-to-date initiation to the field. New to the Second EditionComplements on discrete models, including Rogers'' approach to the fundamental theorem of asset pricing and super-replication in incomplete markets Discussions on local volatility, Dupire''s formula, the change of numéraire techniques, forward measures, and the forward Libor model A new chapter on credit risk modeling An extension of the chapter on simulTrade ReviewThe second edition of this book provides a concise and accessible introduction to the probabilistic techniques needed to understand the most widely used financial models. This edition incorporates many new techniques and concepts to be used to describe the behavior of financial markets. … the solutions obtained using SciLab for computer experiments are available at http://cermics.enpc.fr/~bl/scilab/ These experiments were well designed by the authors based on their teaching and research experience and were found to be effective in communicating these concepts and ideas and enhancing the understanding of readers. … a solid introduction to stochastic approaches used in the financial world. The authors cover many key finance topics … . The book can be used as a reference text by researchers and graduate students in financial mathematics. It also is ideal reading material for practicing financial analysts and consultants using mathematical models for finance.—Technometrics, May 2009, Vol. 51, No. 2 Table of ContentsDiscrete-Time Models. Optimal Stopping Problem and American Options. Brownian Motion and Stochastic Differential Equations. The Black-Scholes Model. Option Pricing and Partial Differential Equations. Interest Rate Models. Asset Models with Jumps. Credit Risk Models. Simulation and Algorithms for Financial Models. Appendix. Bibliography. Index.

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Book SynopsisSince the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of likelihood-free methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book.New in the Third Edition New chapter on sTrade Review"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the author’s webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering."- Chitaranjan Mahapatra, Appeared in ISCB News, January 2020 Table of ContentsIntroduction to biological modelling Representation of biochemical networks Probability models Stochastic simulation Markov processes Chemical and biochemical kinetics Case studies Beyond the Gillespie algorithm Spatially extended systems Bayesian inference and MCMC Inference for stochastic kinetic models Conclusions Appendices

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Book SynopsisThe book addresses the system performance with a focus on the network-enhanced complexities and developing the engineering-oriented design framework of controllers and filters with potential applications in system sciences, control engineering and signal processing areas. Therefore, it provides a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against network-enhanced complexities with applications in sensor networks and mobile robotics. Such a result will be of great importance in the development of novel control and filtering theories including industrial impact.Key Features Provides original methodologies and emerging concepts to deal with latest issues in the control and filtering with an emphasis on a variety of network-enhanced complexities Gives results of stochastic control and filtering distributed control and filtering, and security control of complex nTable of Contents1 Introduction. 2 Finite-Horizon H∞ Control with Randomly Occurring Non-linearities and Fading Measurements. 3. Finite-Horizon H∞ Consensus Control for Multi-Agent Systems with Missing Measurements. 4 Finite-Horizon Distributed H∞ State Estimation with Stochastic Parameters through Sensor Networks. 5 Finite-Horizon Dissipative Control for State-Saturated Discrete Time-Varying Systems with Missing Measurements. 6 Finite-Horizon H∞ Filtering for State-Saturated Discrete Time-Varying Systems with Packet Dropouts. 7 Finite-Horizon Envelope-Constrained H∞ Filtering with Fading Measurements. 8 Distributed Filtering under Uniform Quantizations and Deception Attacks through Sensor Networks. 9 Event-Triggered Distributed H∞ State Estimation with Packet Dropouts through Sensor Networks. 10 Event-Triggered Consensus Control for Multi-Agent Systems in the Framework of Input-to-State Stability in Probability. 11 Event-Triggered Security Control for Discrete-Time Stochastic Systems subject to Cyber-Attacks. 12 Event-Triggered Consensus Control for Multi-Agent Systems subject to Cyber-Attacks in the Framework of Observers.

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Book SynopsisSimple, compact toolkit for designing and analyzing algorithms, with concrete examples from control and communications engineering, artificial intelligence, economic modelling.Trade Review'I highly recommend [this book] to all readers interested in the theory of recursive algorithms and its applications in practice.' Mathematical Reviews'This simple compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy in probability and differential equations … Ideal for graduate students, researchers and practitioners in electrical engineering and computer science, especially those working in control, communications, signal processing and machine learning, this book is also relevant to economics, probability and statistics.' L'Enseignement MathématiqueTable of ContentsPreface; 1. Introduction; 2. Basic convergence analysis; 3. Stability criteria; 4. Lock-in probability; 5. Stochastic recursive inclusions; 6. Multiple timescales; 7. Asynchronous schemes; 8. A limit theorem for fluctuations; 9. Constant stepsize algorithms; 10. Applications; 11. Appendices; References; Index.

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Book SynopsisThis book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method. Later chapters apply the theory of random fields to the numerical solution of elliptic PDEs with correlated random data, discuss the Monte Carlo method, and introduce stochastic Galerkin finite-element methods. Finally, stochastic parabolic PDEs are developed. Assuming little previous exposure to probability and statistics, theory is developed in tandem with state-of-the-art computational methods through worked examples, exercises, theorems and proofs. The set of MATLAB codes included (and downloadable) allows readers to perform computations themselves and solve the teTrade Review'This book gives both accessible and extensive coverage on stochastic partial differential equations and their numerical solutions. It offers a well-elaborated background needed for solving numerically stochastic PDEs, both parabolic and elliptic. For the numerical solutions it presents not only proofs of convergence results of different numerical methods but also actual implementations, here in Matlab, with technical details included … With numerical implementations hard to find elsewhere in the literature, and a nice presentation of new research findings together with rich references, the book is a welcome companion for anyone working on numerical solutions of stochastic PDEs, and may also be suitable for use in a course on computational stochastic PDEs.' Roger Pettersson, Mathematical ReviewsTable of ContentsPart I. Deterministic Differential Equations: 1. Linear analysis; 2. Galerkin approximation and finite elements; 3. Time-dependent differential equations; Part II. Stochastic Processes and Random Fields: 4. Probability theory; 5. Stochastic processes; 6. Stationary Gaussian processes; 7. Random fields; Part III. Stochastic Differential Equations: 8. Stochastic ordinary differential equations (SODEs); 9. Elliptic PDEs with random data; 10. Semilinear stochastic PDEs.

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Book SynopsisMeyn and Tweedie is back! The bible on Markov chains in general state spaces has been brought up to date to reflect developments in the field since 1996 - many of them sparked by publication of the first edition. The pursuit of more efficient simulation algorithms for complex Markovian models, or algorithms for computation of optimal policies for controlled Markov models, has opened new directions for research on Markov chains. As a result, new applications have emerged across a wide range of topics including optimisation, statistics, and economics. New commentary and an epilogue by Sean Meyn summarise recent developments and references have been fully updated. This second edition reflects the same discipline and style that marked out the original and helped it to become a classic: proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background.Trade Review'This second edition remains true to the remarkable standards of scholarship established by the first edition … it will no doubt be a very welcome addition to the literature.' Peter W. Glynn, Prologue to the Second EditionTable of ContentsList of figures; Prologue to the second edition Peter W. Glynn; Preface to the second edition Sean Meyn; Preface to the first edition; Part I. Communication and Regeneration: 1. Heuristics; 2. Markov models; 3. Transition probabilities; 4. Irreducibility; 5. Pseudo-atoms; 6. Topology and continuity; 7. The nonlinear state space model; Part II. Stability Structures: 8. Transience and recurrence; 9. Harris and topological recurrence; 10. The existence of Π; 11. Drift and regularity; 12. Invariance and tightness; Part III. Convergence: 13. Ergodicity; 14. f-Ergodicity and f-regularity; 15. Geometric ergodicity; 16. V-Uniform ergodicity; 17. Sample paths and limit theorems; 18. Positivity; 19. Generalized classification criteria; 20. Epilogue to the second edition; Part IV. Appendices: A. Mud maps; B. Testing for stability; C. Glossary of model assumptions; D. Some mathematical background; Bibliography; Indexes.

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Book SynopsisThis book, first published in 2000, addresses financial mathematics of pricing and hedging derivative securities in uncertain and changing market volatility. The mathematics is introduced through examples and illustrated with simulations, and the modeling approach described is validated and tested on market data. The material is suitable for a one-semester course for graduate students.Trade Review'… provides a good overview to the theoretical and practical problems when dealing with stochastic volatility'. Ralf Korn, Mathematical Methods of Operations Research'… something genuinely new … explained with admirable clarity in this extremely well-written book … [which] is short and to the point, and the production quality is high. Buy it.' Mark Davis, Risk Magazine'… well written and makes ideal reading for a graduate course on mathematical finance. The authors took great care in making their ideas clear. I support this text strongly and recommend it for the intended audience.' P. A. L. Embrechts, Publication of the International Statistical Institute'Thanks to a well-written first chapter on the Black-Scholes theory of derivative pricing, the book is essentially self-contained if one has some basic knowledge in stochastic methods and arbitrage pricing. Its style is largely informal which makes it also accessible to practitioners in the finance industry.' M. Schweizer, Zentralblatt für Mathematik'… an excellent book that succeeds admirably in all its aims. It can satisfy both practitioners and researchers at the same time. It is very well written and it is concise and informative.' Angelos Dassios, The Statistician'I consider this book to be an outstanding achievement. the theory is practically very relevant and scientifically on a high level. The book also serves as a good introduction into the basic ideas of Mathematical Finance, putting emphasis on the techniques of partial differential equations. It can therefore also be recommended to readers with little knowledge about Mathematical Finance.' Monatshefte für MathematikTable of Contents1. The Black-Scholes theory of derivative pricing; 2. Introduction to stochastic volatility models; 3. Scales in mean-reverting stochastic volatility; 4. Tools for estimating the rate of mean-reversion; 5. Symptotics for pricing European derivatives; 6. Implementation and stability; 7. Hedging strategies; 8. Application to exotic derivatives; 9. Application to American derivatives; 10. Generalizations; 11. Applications to interest rates models.

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