Stochastics Books
Springer Limit Theorems for the Riemann ZetaFunction 352 Mathematics and Its Applications
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£132.99
Springer Evolution Processes and the FeynmanKac Formula 353 Mathematics and Its Applications
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£85.49
Springer Unbiased Estimators and their Applications Volume 2 Multivariate Case 362 Mathematics and Its Applications
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£123.49
Springer A Course in Stochastic Processes Stochastic Models and Statistical Inference 34 Theory and Decision Library B
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£170.99
Springer Ordinary and Stochastic Differential Geometry as a Tool for Mathematical Physics 374 Mathematics and Its Applications
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£85.49
Springer Limit Theory for Mixing Dependent Random Variables 378 Mathematics and Its Applications
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£85.49
Springer Unimodality of Probability Measures 382 Mathematics and Its Applications
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£123.49
Springer Asymptotic Theory of Nonlinear Regression 389 Mathematics and Its Applications
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£85.49
Springer Probability Dynamics and Causality Essays in Honour of Richard C Jeffrey
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£44.99
Springer Advection and Diffusion in Random Media Implications for Sea Surface Temperature Anomalies
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£85.49
Springer The Complex Faulting Process of Earthquakes 16 Modern Approaches in Geophysics
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£85.49
Springer Random Evolutions and Their Applications 408 Mathematics and Its Applications
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£44.99
Springer Optimal Control of Random Sequences in Problems with Constraints 410 Mathematics and Its Applications
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£85.49
Springer Distributions with given Marginals and Moment Problems
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£85.49
Springer Nonstandard Analysis Theory and Applications 493 Nato Science Series C
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£237.49
Springer Geometric Sums Bounds for Rare Events with Applications Risk Analysis Reliability Queueing 413 Mathematics and Its Applications
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£123.49
Springer Asymptotic Behaviour of Linearly Transformed Sums of Random Variables Mathematics and Its Applications 416
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£85.49
Springer Random Fields and Stochastic Partial Differential Equations 438 Mathematics and Its Applications
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£85.49
Springer Maximum Entropy and Bayesian Methods Boise Idaho USA 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods 98 Fundamental Theories of Physics
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£123.49
Springer Belief Change 3 Handbook of Defeasible Reasoning and Uncertainty Management Systems
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£170.99
Springer Stochastic Models of Systems 469 Mathematics and Its Applications
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£44.99
Springer Limit Theorems for Random Fields with Singular Spectrum Mathematics and Its Applications
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£85.49
Springer Applications of Lie Algebras to Hyperbolic and Stochastic Differential Equations
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£44.99
Springer Maximum Entropy and Bayesian Methods Garching Germany 1998 Proceedings of the 18th International Workshop on Maximum Entropy and Bayesian Methods of Bayesian Methods of Statistical Analysis 105
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£123.49
Springer geoENV II Geostatistics for Environmental Applications Proceedings of the Second European Conference on Geostatistics for Environmental 10 Quantitative Geology and Geostatistics
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£170.99
Springer Error Control and Adaptivity in Scientific Computing Proceedings of the NATO Advanced Study Institute Antaly Turkey August 921 1998 536 Nato Science Series C
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£85.49
Springer Error Control and Adaptivity in Scientific Computing
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£85.49
Springer Stochastic Processes and Operator Calculus on Quantum Groups 490 Mathematics and Its Applications
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£44.99
Springer Introduction to Infinite Dimensional Stochastic Analysis 502 Mathematics and Its Applications
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£85.49
Springer Random Evolutions and Their Applications New Trends Mathematics Its Applications 504 Mathematics and Its Applications
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£85.49
Springer Geometric Aspects of Probability Theory and Mathematical Statistics 514 Mathematics and Its Applications
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£85.49
Springer Abductive Reasoning and Learning 4 Handbook of Defeasible Reasoning and Uncertainty Management Systems
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£170.99
Springer IUTAM Symposium on Nonlinearity and Stochastic Structural Dynamics Held in Madras Chennai India 48 January 1999 Solid Mechanics Its 85 Solid Mechanics and Its Applications
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£123.49
Springer Recent Developments in InfiniteDimensional Analysis and Quantum Probability Papers in Honour of Takeyuki Hidas 70th Birthday
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£85.49
Springer Performance Analysis of MultiChannel and MultiTraffic on Wireless Communication Networks
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£123.49
Springer Computations with Markov Chains Proceedings of the 2nd International Workshop on the Numerical Solution of Markov Chains
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£123.49
Birkhäuser Adventures in Stochastic Processes
Book SynopsisPreface * 1. Preliminaries: Discrete Index Sets and/or Discrete State Spaces * 2. Markov Chains * 3. Renewal Theory * 4. Point Processes * 5. Continuous Time Markov Chains * 6. Brownian Motion * 7. The General Random Walk * References * IndexTrade Review"Definitely the best textbook for a second course in probability now available. Written with excruciating lucidity, and with an excellent choice of exercises." —Gian-Carlo Rota, The Bulletin of Mathematics Books "In summary, Resnick has succeeded [in writing] a very fine textbook which will become popular among students as well as among professors preparing an introductory course on stochastic processes." —Internationale Mathematische Nachrichten "A splendid book to bring home the value and importance of stochastic processes. Highly recommended." —Choice "There are so many good introductory texts on [stochastic processes] that one can hardly hope to write a better or more attractive one. This book, however, convinced the reviewer that it very likely that the Adventures will beocme a widely used, popular first year graduate text on stochastic processes. The book is flexible, the motivations of deep theories are clear, the examples and exercises are interesting." ---Zentralblatt MATHTable of ContentsPreface * 1. Preliminaries: Discrete Index Sets and/or Discrete State Spaces * 2. Markov Chains * 3. Renewal Theory * 4. Point Processes * 5. Continuous Time Markov Chains * 6. Brownian Motion * 7. The General Random Walk * References * Index
£71.24
Birkhauser Boston Measure Theory and Probability The Wadsworth BrooksCole Mathematics Series
Book Synopsis1 Measure Theory.- 2 Integration.- 3 Fourier Analysis.- Appendix A Metric Spaces.- Appendix C A Non-Measurable Subset of the Interval (0, 1].- References.Trade Review"…the text is user friendly to the topics it considers and should be very accessible…Instructors and students of statistical measure theoretic courses will appreciate the numerous informative exercises; helpful hints or solution outlines are given with many of the problems. All in all, the text should make a useful reference for professionals and students."—The Journal of the American Statistical AssociationTable of Contents1 Measure Theory.- 2 Integration.- 3 Fourier Analysis.- Appendix A Metric Spaces.- Appendix C A Non-Measurable Subset of the Interval (0, 1].- References.
£59.99
Birkhauser Boston A Probability Path Modern Birkhuser Classics
Book SynopsisInstead, A Probability Path is designed for those requiring a deep understanding of advanced probability for their research in statistics, applied probability, biology, operations research, mathematical finance and engineering.Trade ReviewFrom the reviews:“This introduction to measure-theoretic probability is intended for students whose primary interest is not mathematics but statistics, engineering, biology, or finance. The book is a welcome reprint in paperback … . The book’s pace … is ‘quick and disciplined’.” (William J. Satzer, MAA Reviews, March, 2014)Table of Contents1 Sets and Events.- 2 Probability Spaces.- 3 Random Variables, Elements and Measurable Maps.- 4 Independence.- 5 Integration and Expectation.- 6 Convergence Concepts.- 7 Laws of Large Numbers and Sums of Independent Random Variables.- 8 Convergence in Distribution.- 9 Characteristic Functions and the Central Limit Theorem.- 10 Martingales.- Index.- References.
£52.49
Atlantic Financial Press Twenty Lectures About Gaussian Processes
£47.12
Springer New York Statistical Analysis of Designed Experiments Third Edition Springer Texts in Statistics
Book SynopsisThis textbook presents the design and analysis of experiments that comprise the aspects of classical theory for continuous response, modern procedures for categorical response, and especially for correlated categorical response.Trade ReviewFrom the reviews of the second edition:"[This book] is a useful reference or graduate text to complement more common choices for introductory design of experiment books … the methods are logically and thoroughly developed in a rigorous, yet understandable manner. The emphasis on pharmaceutical applications throughout the book is helpful, because this continues to emerge as an important area of applications. The book would be helpful for statisticians and researchers in pharmaceutical areas once they had gained a solid understanding of the fundamentals of design of experiments." –Journal of the American Statistical Association"The second edition of this book … has been reorganized with a list of topics similar to that of the first edition, but with a revised presentation and order. … much greater emphasis now placed on the analysis aspect of design of experiments. … a useful reference book or graduate text … . The methods are logically and thoroughly developed in a rigorous, yet understandable manner. … The book would be helpful for statisticians and researchers in pharmaceutical areas … ." (Christine M. Anderson Cook, Journal of the American Statistical Association, Vol. 98 (463), 2003)"This book is mostly concerned with the mathematical detail of the topics in the contents. There are a few sets of data, to illustrate the material; on these, SAS, S-PLUS or SPSS is used for analysis. … This would be an excellent book for mathematics students who take a course in statistics, or graduate statistic students … ." (N. R. Draper, Short Book Reviews, Vol. 23 (1), 2003)"Helge Toutenburg describes this text as a ‘resource/reference book which contains statistical methods used by researchers in applied areas.’ … the theory is described in a shorthand style that gets to the point without overburdening the reader with mathematical detail … . the author includes thorough discussions of generalized linear models (categorical data analysis) and repeated-measures designs. … a useful, self-contained reference for those who want a quick description of the underlying theory and practice for a large assortment of standard DOE problems." (Peter Wludyka, Technometrics, Vol. 45 (2), May, 2003)From the reviews of the third edition:“This book provides matter related to experimental designs which are of practical relevance. One can understand the subject matter without knowledge of high level mathematics. The book is suitable as a textbook for courses on experimental design in universities and institutions and as a resource book for researchers.” (B. L. Agarwal, Zentralblatt MATH, Vol. 1211, 2011)Table of ContentsComparison of Two Samples.- The Linear Regression Model.- Single#x2013;Factor Experiments with Fixed and Random Effects.- More Restrictive Designs.- Incomplete Block Designs.- Multifactor Experiments.- Models for Categorical Response Variables.- Repeated Measures Model.- Cross#x2013;Over Design.- Statistical Analysis of Incomplete Data.
£123.49
Springer Introducing Monte Carlo Methods with R
Book SynopsisBasic R Programming.- Random Variable Generation.- Monte Carlo Integration.- Controlling and Accelerating Convergence.- Monte Carlo Optimization.- Metropolis#x2013;Hastings Algorithms.- Gibbs Samplers.- Convergence Monitoring and Adaptation for MCMC Algorithms.Trade ReviewFrom the reviews:“Robert and Casella’s new book uses the programming language R, a favorite amongst (Bayesian) statisticians to introduce in eight chapters both basic and advanced Monte Carlo techniques … . The book could be used as the basic textbook for a semester long course on computational statistics with emphasis on Monte Carlo tools … . useful for (and should be next to the computer of) a large body of hands on graduate students, researchers, instructors and practitioners … .” (Hedibert Freitas Lopes, Journal of the American Statistical Association, Vol. 106 (493), March, 2011)“Chapters focuses on MCMC methods the Metropolis–Hastings algorithm, Gibbs sampling, and monitoring and adaptation for MCMC algorithms. … There are exercises within and at the end of all chapters … . Overall, the level of the book makes it suitable for graduate students and researchers. Others who wish to implement Monte Carlo methods, particularly MCMC methods for Bayesian analysis will also find it useful.” (David Scott, International Statistical Review, Vol. 78 (3), 2010)“The primary audience is graduate students in statistics, biostatistics, engineering, etc. who need to know how to utilize Monte Carlo simulation methods to analyze their experiments and/or datasets. … this text does an effective job of including a selection of Monte Carlo methods and their application to a broad array of simulation problems. … Anyone who is an avid R user and has need to integrate and/or optimize complex functions will find this text to be a necessary addition to his or her personal library.” (Dean V. Neubauer, Technometrics, Vol. 53 (2), May, 2011)Table of ContentsBasic R Programming.- Random Variable Generation.- Monte Carlo Integration.- Controlling and Accelerating Convergence.- Monte Carlo Optimization.- Metropolis#x2013;Hastings Algorithms.- Gibbs Samplers.- Convergence Monitoring and Adaptation for MCMC Algorithms.
£59.99
Springer New York Nonparametric Functional Data Analysis Theory and Practice Springer Series in Statistics
Book SynopsisAt the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.Trade ReviewFrom the reviews: "This is certainly a very valuable book for anyone interested in this new methodology." N.D.C. Veraverbeke for Short Book Reviews of the ISI, December 2006 "The present book does bring something new and, indeed some novel theoretical investigations into the kinds of functional data problems … . I do think the present book is a worthy contribution to the literature. The authors have done a nice job of summarizing some of ongoing research … . Researchers in the growing functional statistics community should be glad to have a copy of the book." (Z. Q. John Lu, Technometrics, Vol. 49 (2), 2007) "This book presents new nonparametric staustical methods for samples of functional data … . The computational aspects of the book are oriented toward practitioners whereas open problems emerging from this new field of statistics will attract Ph. D. students and academic researchers. This book is also accessible to graduate students starting out in the area of functional statistics." (Fazil A. Aliev, Mathematical Reviews, Issue 2007 b) "Nonparametric Functional Data Analysis explores nonparametric methods as that can be applied to functional data, developing new methods and providing theoretical results for the conditional and unconditional mean, median, and mode for independent and dependent functional data. … As a resource for those interested in FDA research and methods, it is highly recommended. … This book should spur new and exciting research in FDA, and it provides new tools that are ready for application to real data sets." (Mark Greenwood, Journal of the American Statistical Association, Vol. 102 (479), 2007) "Example data sets that motivate the development of the models are also provided. … The index provided seems to be fairly complete and is helpful in looking up topics discusses in this monograph. Several chapters end in a section in which the authors provide additional comments, discussions and pose some open problems in this area, which should be appealing for researchers in this field. … This book should be useful for all people interested in the area of functional data analysis." (Anatolij Dvurecenskij, Zentralblatt MATH, Vol. 1119 (21), 2007)Table of ContentsIntroduction to functional nonparametric statistics.- Some functional datasets and associated statistical problematics.- What is a well adapted space for functional data?.- Local weighting of functional variables.- Functional nonparametric prediction methodologies.- Some selected asymptotics.- Computational issues.- Nonparametric supervised classification for functional data.- Nonparametric unsupervised classification for functional data.- Mixing, nonparametric and functional statistics.- Some selected asymptotics.- Application to continuous time processes prediction.- Small ball probabilities, semi-metric spaces and nonparametric statistics.- Conclusion and perspectives.
£104.99
Springer Elementary Probability Theory
Book Synopsis1 Set.- 1.1 Sample sets.- 1.2 Operations with sets.- 1.3 Various relations.- 1.4 Indicator.- Exercises.- 2 Probability.- 2.1 Examples of probability.- 2.2 Definition and illustrations.- 2.3 Deductions from the axioms.- 2.4 Independent events.- 2.5 Arithmetical density.- Exercises.- 3 Counting.- 3.1 Fundamental rule.- 3.2 Diverse ways of sampling.- 3.3 Allocation models; binomial coefficients.- 3.4 How to solve it.- Exercises.- 4 Random Variables.- 4.1 What is a random variable?.- 4.2 How do random variables come about?.- 4.3 Distribution and expectation.- 4.4 Integer-valued random variables.- 4.5 Random variables with densities.- 4.6 General case.- Exercises.- Appendix 1: Borel Fields and General Random Variables.- 5 Conditioning and Independence.- 5.1 Examples of conditioning.- 5.2 Basic formulas.- 5.3 Sequential sampling.- 5.4 Pólya's urn scheme.- 5.5 Independence and relevance.- 5.6 Genetical models.- Exercises.- 6 Mean, Variance, and Transforms.- 6.1 Basic properties of expectationTrade Review"In spite of the original edition of the book being nearly thirty years old, the text still has its role to play in first and second year undergraduate probability courses. It provides an excellent foundation to more advanced courses in the subject."Short Book Reviews, Vol. 23/3, Dec. 2003 "This edition is the third revision of a text on mathematical probability first published in 1974. The text is aimed at undergraduate mathematics students and is accessible to a general audience. The prose is accurate, entertaining, and dense with historical tidbits. Two concluding chapters on mathematical finance have been added to the eight chapters in the third edition by the second author." The American Statistician, May 2004 From the reviews of the fourth edition: "The main novelty in the fourth edition of this well-written book is the addition of new chapters … . The new chapters share the friendly yet rigorous style of the former ones. They begin with an account of the financial vocabulary, which is then expounded in probabilistic terms. … Almost thirty years after its first edition, this charming book continues to be an excellent text for teaching and for self study." (Ricardo Maronna, Statistical Papers, Vol. 45 (4), 2004)Table of ContentsSet * Probability * Counting * Random Variables * Conditioning and Independence * Mean, Variance and Transforms * Poisson and Normal Distributions * From Random Walks to Markov Chains * Mean-Variance Pricing Model * Option Pricing Theory
£49.99
Springer An Introduction to Probabilistic Modeling
Book Synopsis1 Basic Concepts and Elementary Models.- 1. The Vocabulary of Probability Theory.- 2. Events and Probability.- 3. Random Variables and Their Distributions.- 4. Conditional Probability and Independence.- 5. Solving Elementary Problems.- 6. Counting and Probability.- 7. Concrete Probability Spaces.- Illustration 1. A Simple Model in Genetics: Mendel's Law and HardyWeinberg's Theorem.- Illustration 2. The Art of Counting: The Ballot Problem and the Reflection Principle.- Illustration 3. Bertrand's Paradox.- 2 Discrete Probability.- 1. Discrete Random Elements.- 2. Variance and Chebyshev's Inequality.- 3. Generating Functions.- Illustration 4. An Introduction to Population Theory: GaltonWatson's Branching Process.- Illustration 5. Shannon's Source Coding Theorem: An Introduction to Information Theory.- 3 Probability Densities.- I. Expectation of Random Variables with a Density.- 2. Expectation of Functionals of Random Vectors.- 3. Independence.- 4. Random Variables That Are Not Discrete anTable of Contents1 Basic Concepts and Elementary Models.- 1. The Vocabulary of Probability Theory.- 2. Events and Probability.- 2.1. Probability Space.- 2.2. Two Elementary Probabilistic Models.- 3. Random Variables and Their Distributions.- 3.1. Random Variables.- 3.2. Cumulative Distribution Function.- 4. Conditional Probability and Independence.- 4.1. Independence of Events.- 4.2. Independence of Random Variables.- 5. Solving Elementary Problems.- 5.1. More Formulas.- 5.2. A Small Bestiary of Exercises.- 6. Counting and Probability.- 7. Concrete Probability Spaces.- Illustration 1. A Simple Model in Genetics: Mendel’s Law and Hardy—Weinberg’s Theorem.- Illustration 2. The Art of Counting: The Ballot Problem and the Reflection Principle.- Illustration 3. Bertrand’s Paradox.- 2 Discrete Probability.- 1. Discrete Random Elements.- 1.1. Discrete Probability Distributions.- 1.2. Expectation.- 1.3. Independence.- 2. Variance and Chebyshev’s Inequality.- 2.1. Mean and Variance.- 2.2. Chebyshev’s Inequality.- 3. Generating Functions.- 3.1. Definition and Basic Properties.- 3.2. Independence and Product of Generating Functions.- Illustration 4. An Introduction to Population Theory: Galton—Watson’s Branching Process.- Illustration 5. Shannon’s Source Coding Theorem: An Introduction to Information Theory.- 3 Probability Densities.- I. Expectation of Random Variables with a Density.- 1.1. Univariate Probability Densities.- 1.2. Mean and Variance.- 1.3. Chebyshev’s Inequality.- 1.4. Characteristic Function of a Random Variable.- 2. Expectation of Functionals of Random Vectors.- 2.1. Multivariate Probability Densities.- 2.2. Covariance, Cross-Covariance, and Correlation.- 2.3. Characteristic Function of a Random Vector.- 3. Independence.- 3.1. Independent Random Variables.- 3.2. Independent Random Vectors.- 4. Random Variables That Are Not Discrete and Do Not Have a pd.- 4.1. The Abstract Definition of Expectation.- 4.2. Lebesgue’s Theorems and Applications.- Illustration 6. Buffon’s Needle: A Problem in Random Geometry.- 4 Gauss and Poisson.- 1. Smooth Change of Variables.- 1.1. The Method of the Dummy Function.- 1.2. Examples.- 2. Gaussian Vectors.- 2.1. Characteristic Function of Gaussian Vectors.- 2.2. Probability Density of a Nondegenerate Gaussian Vector.- 2.3. Moments of a Centered Gaussian Vector.- 2.4. Random Variables Related to Gaussian Vectors.- 3. Poisson Processes.- 3.1. Homogeneous Poisson Processes Over the Positive Half Line.- 3.2. Nonhomogeneous Poisson Processes Over the Positive Half Line.- 3.3. Homogeneous Poisson Processes on the Plane.- 4. Gaussian Stochastic Processes.- 4.1. Stochastic Processes and Their Law.- 4.2. Gaussian Stochastic Processes.- Illustration 7. An Introduction to Bayesian Decision Theory: Tests of Gaussian Hypotheses.- 5 Convergences.- 1. Almost-Sure Convergence.- 1.1. The Borel—Cantelli Lemma.- 1.2. A Criterion for Almost-Sure Convergence.- 1.3. The Strong Law of Large Numbers.- 2. Convergence in Law.- 2.1. Criterion of the Characteristic Function.- 2.2. The Central Limit Theorem.- 3. The Hierarchy of Convergences.- 3.1. Almost-Sure Convergence Versus Convergence in Probability.- 3.2. Convergence in the Quadratic Mean.- 3.3. Convergence in Law in the Hierarchy of Convergences.- 3.4. The Hierarchical Tableau.- Illustration 8. A Statistical Procedure: The Chi-Square Test.- Illustration 9. Introduction to Signal Theory: Filtering.- Additional Exercises.- Solutions to Additional Exercises.
£71.96
Springer New York What Is Random Chance and Order in Mathematics and Life
Book Synopsis[1] The Taming of Chance.- From Unpredictable to Lawful.- Probability.- Order in the Large.- The Normal Law.- Is It Random?.- More About the Law of Large Numbers.- Where We Stand Now.- [2] Uncertainty and Information.- Messages and Information.- Entropy.- Messages, Codes, and Entropy.- Approximate Entropy.- Again, Is It Random?.- The Perception of Randomness.- [3] Janus-Faced Randomness.- Is Determinism an Illusion?.- Generating Randomness.- Janus and the Demons.- [4] Algorithms, Information, and Chance.- Algorithmic Randomness.- Algorithmic Complexity and Undecidability.- Algorithmic Probability.- [5] The Edge of Randomness.- Between Order and Disorder.- Self-Similarity and Complexity.- What Good is Randomness?.- Sources and Further Readings.- Technical Notes.- Appendix A: Geometric Sums.- Appendix B: Binary Numbers.- Appendix C: Logarithims.- References.Trade ReviewFrom the reviews: THE AMERICAN STATISTICIAN "In summary, I think that many readers with a strong interest in mathematics, statistics, physics, or other areas of science will find this book interesting and challenging. I strongly recommend it to all who are interested in science and would like to see how the ideas of both theoretical mathematics and statistics have been observed and used in real life throughout history." MATHEMATICAL REVIEWS "The book is nicely written and should entertain many readers…"Table of Contents[1] The Taming of Chance.- From Unpredictable to Lawful.- Probability.- Order in the Large.- The Normal Law.- Is It Random?.- More About the Law of Large Numbers.- Where We Stand Now.- [2] Uncertainty and Information.- Messages and Information.- Entropy.- Messages, Codes, and Entropy.- Approximate Entropy.- Again, Is It Random?.- The Perception of Randomness.- [3] Janus-Faced Randomness.- Is Determinism an Illusion?.- Generating Randomness.- Janus and the Demons.- [4] Algorithms, Information, and Chance.- Algorithmic Randomness.- Algorithmic Complexity and Undecidability.- Algorithmic Probability.- [5] The Edge of Randomness.- Between Order and Disorder.- Self-Similarity and Complexity.- What Good is Randomness?.- Sources and Further Readings.- Technical Notes.- Appendix A: Geometric Sums.- Appendix B: Binary Numbers.- Appendix C: Logarithims.- References.
£12.39
Springer New York A History of the Central Limit Theorem From Classical to Modern Probability Theory Sources and Studies in the History of Mathematics and Physical Sciences
Book SynopsisThis study discusses the history of the central limit theorem and related probabilistic limit theorems from about 1810 through 1950.Trade ReviewFrom the book reviews:“Fischer provides thorough mathematical descriptions of the development of the central limit theorem as it evolves with increasing mathematical rigor. … Fischer has probably written what will be the definitive history of the central limit theorem for many years to come. … Fischer overflows with detail, insight and excellent commentary.” (David Bellhouse, Historia Mathematica, Vol. 39, 2012)“The book will be of interest not only to professionals in the area of probability and statistics but to a wider audience. … The author has been using a huge amount of sources and archives, including his own works, and he is successful in his goal to describe a comprehensive picture of the development of the CLT. … the book would be an excellent source for student projects on topics from probability and its applications.” (Jordan M. Stoyanov, Zentralblatt MATH, Vol. 1226, 2012)“This work details the history of the central limit theorem and related probabilistic limit theorems roughly from 1810 through 1950, but focuses on 1810 to 1935. … Hans Fischer … authors many papers on the history of mathematics. His skill in both these areas allows him to reveal here the historical development of this important theorem in a way that can easy be adapted to the lecture hall or used in independent study.” (Tom Schulte, The Mathematical Association of America, February, 2011)“The history of the CLT deserves a place of its own, and this book by Hans Fischer is the best … in tracing its development in meticulous historical detail and with mathematical precision. … The book by Hans Fischer is highly recommended as a well-researched comprehensive history of the CLT. One finds here the story of a galaxy of brilliant mathematicians … engaging in the quest for, and debates on, the true meaning and the correct derivation of a beautiful intriguing result.” (Rabi Bhattacharya, SIAM Review, Vol. 53 (4), 2011)Table of ContentsPreface.- Introduction.- The central limit theorem from laplace to cauchy: changes in stochastic objectives and in analytical methods.- The hypothesis of elementary errors.- Chebyshev's and markov's contributions.- The way towards modern probability.- General limit problems.- Conclusion: the central limit theorem as a link between classical and modern probability.- Index.- Bibliography
£159.99
Springer New York Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis 22 Information Science and Statistics
Book SynopsisThe techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.Trade ReviewFrom the book reviews:“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)Table of ContentsIntroduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.
£82.49