Probability and statistics Books

Book SynopsisWe utilize statistics when we evaluate TV program ratings, predict voting outcomes, prepare stock, predict the amount of sales, and evaluate the effectiveness of medical treatment. We want to predict the results not on the basis of personal experience or images, but on the basis of corresponding data. The accuracy of the prediction depends on the data and related theories. It is easy to show input and output data associated with a model without understanding it. However, the models themselves are not perfect, because they contain assumptions and approximations in general. Therefore, the application of the model to the data should be careful. We should know what model we should apply to the data, what parameters are assumed in the model, and what we can state based on the results of the models.Let us consider a coin toss, for example. When we perform a coin toss, we obtain a head or a tail. If we try the toss a coin three times, we may obtain the results of two heads and one tail. Therefore, the probability that we obtain for heads is , and the one that we obtain for tails is . This is a fact and we need not to discuss this any further. It is important to notice that the probability ( ) of getting a head is limited to this trial. Therefore, we can never say that the probability that we obtain for heads with this coin is , in which we state general characteristics of the coin. If we perform the coin toss trial 400 times and obtain heads 300 times, we may be able to state that the probability of obtaining a head is as the characteristics of the coin. What we can state based on the obtained data depends on the sample number. Statistics gives us a clear guideline under which we can state something is based on the data with corresponding error ranges.Mathematics used in statistics is not so easy. It may be tough work to acquire the related techniques. Fortunately, software development makes it easy to obtain results. Therefore, many members who are not specialists in mathematics can perform statistical analysis with these types of software. However, it is important to understand the meaning of the model, that is, why some certain variables are introduced and what they express, and what we can state based on the results. Therefore, understanding mathematics related to the models is invoked to appreciate the results.In this book, the authors treat models from fundamental ones to advanced ones without skipping their derivation processes. It is then possible to clearly understand the assumptions and approximations used in the models, and hence understand the limitation of the models.The authors also cover almost all the subjects in statistics since they are all related to each other, and the mathematical treatments used in a model are frequently used in the other ones.Additionally, many good practical and theoretical books on statistics are presented [1]-[10]. However, these books are oriented to special cases: Fundamental, mathematical, or special subjects. The author also aims to connect theories to practical subjects. He hopes that this book will aid readers in furthering their knowledge of special cases in statistics.

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Book SynopsisExponential distribution is one of the most-used distributions in the theory and practice of statistics. It has several important properties like being memoryless and having a constant hazard rate. The field of characterization is developed in different branches of statistics and applied probability. Ordered random variables are common in various applications in practice. In this book, characterizations of exponential distribution using ordered random variables are presented. Most of the known results as well as many new results are given in this book. The aim of the book is to present various characterizations of exponential distribution based on ordered random variables. The book is written on a lower technical level and requires basic knowledge of mathematics and statistics. Chapter 1 gives some basic properties of exponential distribution. Chapters 2, 3, and 4 give the characterization of exponential distribution based on order statistics, record values, and generalized order statistics.Table of ContentsFor more information, please visit our website at:https://novapublishers.com/shop/characterizations-of-exponential-distribution-by-ordered-random-variables/

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Book SynopsisThis book presents and discusses new developments in the study of evolutionary processes. Topics discussed include evolution of magneto-acoustic waves in isothermal atmosphere, quantum dynamical semigroups, traveling waves in discrete models of biological population, motion of electrorheological fluids, Stacklberg control of a backward linear heat equation, Leray weak solutions of Navier-Stokes equation involving one directional derivative, and initial value boundary problem of an evolutionary p(x)-Laplacian equation.

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Book SynopsisThe purpose of this book is to help the users of computerised statistical packages make the correct statistical choices to match the data they have already collected. It is assumed that the reader has information available on the use of the statistical package. While most statistical packages will perform whatever statistical analyses the user chooses, the package will not help the user in the selection of the choice or in the interpretation of the results. Thus, a statistical package is only a tool. While statistical packages come with instructions on their use, the user must know how to apply the package to the data available and how to read the results printed. The book provides a simple to understand, step-by-step guide for the user of any computerised statistical package. This book is designed to bivariate (two variables tested together) data analysis.

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Book SynopsisInequality Theory & Applications

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Book SynopsisThe aim of this volume is to introduce and exchange recent new topics in the areas of probability theory and applications in inequality theory, stochastic analysis and applications.

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Book SynopsisProbability theory is the mathematical theory of random (non-deterministic) phenomena. This book presents the latest research in the field.

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Book SynopsisThe aim of this volume is to introduce and exchange recent new topics on the areas of inequality theory and their applications dealing in pure and applied mathematics.

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Book SynopsisIn this book, generally speaking, some properties of bitopological spaces generated by certain non-symmetric functions are studied. These functions, called "probabilistic quasi-pseudo-metrics" and "fuzzy quasi-pseudo-metrics", are generalisations of classical quasi-pseudo metrics. For the sake of completeness as well as for convenience and easy comparison, most of the introductory paragraphs are mainly devoted to fundamental notions and results from the classical -- deterministic or symmetric -- theory.

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Book SynopsisThe ordered random variables play important roles in the theory and practice of statistics. They possess significant statistical properties. Over the last few decades, many articles on various topics of ordered statistical data have appeared. Our handbook comprises twenty one chapters discussing various topics on theory and applications. The editors of this book worked together several articles on order and record statistics, which covered the subjects of distributional properties, characterisations and statistical inferences. It was a special interest to co-ordinate and edit an interesting research problem based on material contributed by several prominent researchers from all over the world. This book presents new developments in the subject of ordered random variables. These aspects involve theory of ordered random variables, reliability theory, stochastic ordering, bounds, characterisations, and estimation and prediction techniques.

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Book SynopsisComputers have taken a permanent place in almost every human endeavor in the last 20 years. This infiltration requires a learning process on the part of the people utilizing them and realizing where and how computers can be best used beyond the basic and obvious applications. Statistics is an example of their application in many diverse fields to reach conclusions and make projections never before possible. Beyond this, applied statistics is rapidly becoming not only an instrument, but an integral part of the advance of knowledge. There are many fields such as medicine, biology, weather prediction, military planning, and numerous others where the statistical studies are essential before the next step can be taken. This new book presents the latest research in the field from around the globe.

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Book SynopsisIs the degree of probability that an individual holds when betting on a particular outcome really so subjective that, as Frank Ramsey once argued, "objective logical relations" do not exist and that "probability is ''the logic of partial belief"? (WK 2012a) This subjective interpretation of probability can be contrasted with an objective view by John Keynes, who argued instead that "logical probabilities are conceived to be objective, logical relations between propositions (or sentences), and hence not to depend in any way upon belief". (WK 2012) Contrary to these opposing interpretations (and other ones as will be discussed in the book), probability (in relation to both objectivity and subjectivity) are neither possible (nor impossible) nor desirable (or undesirable) to the extent that the respective ideologues (on different sides) would like us to believe. Surely, this questioning of the opposing interpretations on probability does not entail that probability is useless, or that those fields related to probability (like "statistics, finance, gambling, science, artificial intelligence/machine learning and philosophy") are not worth studying. Needless to say, neither of these extreme views is reasonable. Instead, this book offers an alternative way to understand the future of probability, especially in the dialectic context of objectivity and subjectivity -- while learning from different approaches in the literature but without favouring any one of them (nor integrating them, since they are not necessarily compatible with each other). More specifically, this book offers a new theory (that is, the interpretivist theory of probability) in order to go beyond the existing approaches in a novel way. To understand this, the book is organised in four chapters. This seminal project will fundamentally change the way that we think about probability in relation to objectivity and subjectivity from the combined perspectives of the mind, nature, society, and culture, with enormous implications for the human future and what I originally called its "post-human" fate.

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Book SynopsisThis book makes a significant contribution to the advancement of statistical science. It contains research in many statistical designs, compares many statistical models, and includes a theory that is oriented to real life problems.

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Book SynopsisWhether it’s an unforeseen financial crash, a shock election result or a washout summer that threatens to ruin a holiday in the sun, forecasts are part and parcel of our everyday lives. We rely wholeheartedly on them, and become outraged when things don’t go exactly to plan. But should we really put so much trust in predictions? Perhaps gut instincts can trump years of methodically compiled expert knowledge? And when exactly is a forecast not a forecast? Forewarned will answer all of these intriguing questions, and many more. Packed with fun anecdotes and startling facts, Forewarned is a myth-busting guide to prediction, based on the very latest scientific research. It lays out the many ways forecasting can help us make better decisions in an unpredictable modern world, and reveals when forecasts can be a reliable guide to the uncertainties of the future – and when they are best ignored.

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Book SynopsisAs the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. This book covers David Marr's paradigm and various underlying statistical models for vision.

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Book SynopsisNever before have we attempted to measure as much as we do today. Why are we so obsessed with numbers? What can they really tell us?Trade Review‘A great antidote to cynicism, and a sharply witty reminder of what is important in life.’ Independent ‘Wonderfully subversive.’ Guardian.

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Book SynopsisThis book is a practical guide to the use of Monte Carlo simulation techniques for the study of electron solid interactions in the electron microscope. Programs, optimized for use on personal computers, are developed to deal with typical applications including secondary, and back- scattered, electron imaging. EBIC imaging of semiconductors and X-ray microanalysis.Trade Review`... provides an outstanding introduction for the microscopist seeking to make new use of this powerful simulation tool, as well as a great resource for established modelers looking to extend their knowledge... clearly written and strongly supported by practical examples throughout.' Radiation and Physical ChemistryTable of ContentsPreface ; 1. An Introducton to Monte Carlo Methods ; 2. Constructing a Simulation ; 3. The Single Scattering Model ; 4. The Plural Scattering Model ; 5. Practical Applications of Monte Carlo Models ; 6. Backscattered Electrons ; 7. Charge Collection Microscopy and Cathodoluminescence ; 8. Secondary Electrons and Imaging ; 9. X-ray Production and Micro-Analysis ; 10. What Next in Monte Carlo Simulations?

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Book SynopsisSignal detection theory, as developed in electrical engineering and based on statistical decision theory, was first applied to human sensory discrimination about 40 years ago. The theory''s intent was to explain how humans discriminate and how we might use reliable measures to quantify this ability. An interesting finding of this work is that decisions are involved even in the simplest of discrimination tasks--say, determining whether or not a sound has been heard (a yes-no decision). Detection theory has been applied to a host of varied problems (for example, measuring the accuracy of diagnostic systems, survey research, reliability of lie detection tests) and extends far beyond the detection of signals. This book is a primer on signal detection theory, useful for both undergraduates and graduate students.Trade Review"This book contains the theoretical explications of the ways observers detect weak, uncertain, or ambiguous signals. It explains the math underlying the theory, and outlines its uses in measuring an observer's sensitivity. The book is intended to serve as an introductory text for undergraduate or graduate courses in sensation and perception, psychophysics, cognition, and quantitative methods; it may also be used as a reference for researchers. Wickens teaches at the University of California, Los Angeles."--SciTech Book News "This book contains the theoretical explications of the ways observers detect weak, uncertain, or ambiguous signals. It explains the math underlying the theory, and outlines its uses in measuring an observer's sensitivity. The book is intended to serve as an introductory text for undergraduate or graduate courses in sensation and perception, psychophysics, cognition, and quantitative methods; it may also be used as a reference for researchers. Wickens teaches at the University of California, Los Angeles."--SciTech Book NewsTable of Contents1. The signal-detection model ; 2. The equal-variance Gaussian model ; 3. Operating characteristics and the Gaussian model ; 4. Measures of detection performance ; 5. Confidence ratings ; 6. Forced-choice procedures ; 7. Discrimination and identification ; 8. Finite-state models ; 9. Likelihoods and likelihood ratios ; 10. Multidimensional stimuli ; 11. Statistical treatment ; A. A summary of probability theory ; References

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Book SynopsisThis book presents the statistical methods that are useful in the study of molecular evolution and illustrates how to use them in actual data analysis. Molecular evolution has been developing at a great pace over the past decade or so, driven by the huge increase in genetic sequence data from many organisms, the improvement of high-speed microcomputers, and the development of several new methods for phylogenetic analysis. This book for graduate students and researchers, assuming a basic knowledge of evolution, molecular biology, and elementary statistics, should make it possible for many investigators to incorporate refined statistical analysis of large-scale data in their own work. Nei is one of the leading workers in this area. He and Kumar have developed a computer program called MEGA, which has been sold for about $20 to over 1900 users. For the book, the authors are thoroughly revising MEGA and will make it available via FTP. The book also included analysis using the other most poTrade ReviewIt is worth its price * Plant Systematics and Evolution *Table of Contents1. Molecular basis of evolution ; 2. Evolutionary changes of amino acid sequences ; 3. Evolutionary changes of DNA sequences ; 4. Synonymous and nonsynonymous nucleotide substitutions ; 5. Phylogenetic trees ; 6. Phylogenetic inference: Distance methods ; 7. Phylogenetic inference: Maximum parsimony methods ; 8. Phylogenetic inference: Maximum likelihood methods ; 9. Accuracies and statistical tests of phylogenetic trees ; 10. Molecular clocks and linearized trees ; 11. Ancestral nucleotide and amino acid sequences ; 12. Genetic polymorphism and evolution ; 13. Population trees from genetic markers ; 14. Perspectives ; Appendices ; A. Mathematical sumbols and notations ; B. Geological timescale ; C. Geological events in the Cenozoic and Meszoic eras ; D. Evolution of organisms based on the fossil record

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Book SynopsisThis book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).Trade Review"Glen Cowan is a particle physicist who seems to have got everything right. Results are stated clearly, without mathematical proof but with enough explanation to satisfy the physicist's need to understand not only how, but also why...Those teaching an advanced undergraduate or graduate course in statistics or physicists will find this a good textbook...Do not be fooled by the fact that it does not have the "textbook look" - the exercises have been made available separately on a Web site. " CERN CourierTable of ContentsPreface ; Notation ; 1. Fundamental Concepts ; 2. Examples of Probability Functions ; 3. The Monte Carlo Method ; 4. Statistical Tests ; 5. General Concepts of Parameter Estimation ; 6. The Method of Maximum Likelihood ; 7. The Method of Least Squares ; 8. The Method of Moments ; 9. Statistical Errors, Confidence Intervals and Limits ; 10. Characteristic Functions and Related Examples ; 11. Unfolding ; Bibliography ; Index

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Book SynopsisAnother title in the reissued Oxford Classic Texts in the Physical Sciences series, Jeffrey''s Theory of Probability, first published in 1939, was the first to develop a fundamental theory of scientific inference based on the ideas of Bayesian statistics. His ideas were way ahead of their time and it is only in the past ten years that the subject of Bayes'' factors has been significantly developed and extended. Until recently the two schools of statistics (Bayesian and Frequentist) were distinctly different and set apart. Recent work (aided by increased computer power and availability) has changed all that and today''s graduate students and researchers all require an understanding of Bayesian ideas. This book is their starting point.Trade ReviewThough mathematically very demanding, the principles and examples are clear enough, and Jeffreys' trenchant references to critics of Bayesian methods often made me laugh out loud - an unusual experience with an advanced statistics text. Its selection as an Oxford Classic Text is unusual but well judged. * Robert Matthews, New Scientist *Table of Contents1. Fundamental notions ; 2. Direct probabilities ; 3. Estimation problems ; 4. Approximate methods and simplifications ; 5. Significance tests: one new parameter ; 6. Significance tests: various complications ; 7. Frequency definitions and direct methods ; 8. General questions

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Book SynopsisLikelihood methods play a central role in statistical theory and methodology. Recently, a new approach to likelihood inference has been developed that often leads to substantial improvements over classical methods. This book gives a detailed introduction to this modern theory of likelihood methods.Trade ReviewThis book is an excellent account of likelihood-based statistical inference; I believe that it will be a very useful addition to any scholarly library. * Biometrics *

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Book SynopsisThe idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly, Trade Review...this is an excellent reference book for those who are interested in studying the mathematical theory for graphical models. * Short Book Reviews, vol. 18, no. 1, April 1998 *Table of ContentsIntroduction ; 1. Graphs and Hypergraphs ; 2. Conditional Independence and Markov Properties ; 3. Contingency Tables ; 4. Multivariate Normal Models ; 5. Models for Mixed Data ; 6. Further topics ; A Various Prerequisites ; B Linear Algebra and Random Vectors ; C The Multivariate Distribution ; D Exponential Models

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Book SynopsisThis is a state-of-the-art summary of a wide range of topics within the field of Descriptive Multivariate Analysis. Each chapter is by an acknowledged authority in the field and provides a readable, comprehensive coverage of the topic for the practising statistician.Table of ContentsForeword ; 1. Clustering from the perspective of combinatorial data analysis ; 2. Developments in principal component analysis ; 3. Canonical discriminant analysis: comparison of resampling methods and convex-hull approximation ; 4. Nonlinear methods for the analysis of homogeneity and heterogeneity ; 5. Principles component models for patterned covariance matrices, with applications to canonical correlation analysis of several sets of variables ; 6. Orthogonal and projection Procrustes analysis ; 7. Graphical Modelling ; 8. Convergent computation by iterative majorization: theory and applications in multidimensional data analysis ; 9. Biplot display of multivariate categorical data, with comments on multiple correspondence analysis ; 10. MANOVA biplots for two-way contingency tables ; 11. Some tools for the multivariate analysis of functional data ; 12. A general theory of biplots ; References

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Book SynopsisBayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.Trade ReviewThe book will certainly be appreciated by researchers and graduate students in computer science, mathematics and philosophy and, in particular, by all interested in the complicated relations between subjective and objective interpretations of probabilistic phenomena. * EMS Newsletter *Bayesian Nets and Causality is a very well-written and well-organized book ... No doubt it will be recognized as a very important contribution to the philosophy of probability and causality by a young distinguished philosopher. * Sungho Choi, Mind *Table of ContentsIntroduction ; Probability ; Bayesian Nets ; Causal Nets: Foundational Problems ; Objective Bayesianism ; Two-Stage Bayesian Nets ; Causality ; Discovering Causal Relationships ; Epistemic Causality ; Recursive Causality ; Logic ; Language Change ; References ; Index

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Book SynopsisIn the theory of random processes there are two that are fundamental, and occur over and over again, often in surprising ways. There is a real sense in which the deepest results are concerned with their interplay. One, the Bachelier Wiener model of Brownian motion, has been the subject of many books. The other, the Poisson process, seems at first sight humbler and less worthy of study in its own right. Nearly every book mentions it, but most hurry past to more general point processes or Markov chains. This comparative neglect is ill judged, and stems from a lack of perception of the real importance of the Poisson process. This distortion partly comes about from a restriction to one dimension, while the theory becomes more natural in more general context. This book attempts to redress the balance. It records Kingman''s fascination with the beauty and wide applicability of Poisson processes in one or more dimensions. The mathematical theory is powerful, and a few key results often produTrade Review'provides an enjoyable and clearly written introduction to the structure and properties of Poisson processes ... Thanks to skillful steering away from, and around, technicalities, it is widely accessible. If you don't know the story, read this book - you will then know what you are missing. If you do know it, a browse through the book, particularly the later chapters, is still worthwhile for interesting perspectives on several areas.' P.J. Donnelly, Queen Mary and Westfield College, Short Book Reviews (Publication of the International Statistical Institute)'It records the author's fascination with the beauty and wide applicability of Poisson processes in one or more dimensions.' L'Enseignement Mathématique, 3-4, 1993'Every mathematician with some knowledge of stochastic processes is aware of the interest and importance of the Poisson process. Therefore it is very useful to have now a book which is devoted to a systematic treatment of Poisson processes. The book ... fulfills the expectations one might have when a famous elder author writes a book on a classic topic. It gives the basic facts in a clear and lucid way. It is shown how the theory can be applied to interesting problems of astronomy, queueing and traffic, etc., and these examples are studied very thoroughly and deeply, giving even the specialist new insights ... an excellent basis for lectures or seminars .... a valuable gift for a young mathematician to stimulate his or her interest in stochastic processes and in applied probability in general.' Mathematical Reviews, Issue 94a'The presentation everywhere is rigorous without being fuzzy about measure theoretical details; this would make the monograph suitable for many readrs, who are either not interested or not trained in measure theoretical subtleties ... a useful addition to the literature both for various beginners as well as for lecturers in the theory of stochastic processes who would find in it a rich array of topics presented clearly.' S.D. Chatterji, Mathematics Abstracts, 773/93The Poisson process is surely the most beautiful object in probability theory, and John Kingman is its most gifted expositor. One might have been forgiven for thinking that there would be little new to say, but in fact this book is studded with new and fascinating insights. It is rare to find a book that simultaneously addresses the beginner and the expert. If there were a prize for the wisest probability book of the decade it would have to go to Bristol's Vice-Chancellor. * David Kendall, Cambridge, Journal of Royal Statistical Society, 1994 *Table of ContentsStochastic models for random sets of points; Poisson processes in general spaces; Sums over Poisson processes; Poisson processes on the line; Marked Poisson processes; Cox processes; Stochastic geometry; Complete random measures; The Poisson-Dirichlet distribution.

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Book SynopsisThis book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine.Trade ReviewStatistical results are given a methodical treatment. In each chapter statistical results are motivated, stated and usually proven, and then illustrated on a variety of datasets based on actual trials. Many of the results were developed by Pepe and her colleagues, who have advanced the field in seminal ways. Chapters end with a concluding remarks section, a set of exercises, and proofs of more involved theoretical results, when needed. Concluding remarks sections nicely summarize results and discuss open research questions. References to key papers are given throughout. This structure allows the book to serve as both a classroom text and an excellent reference to the current literature. * Journal of Biopharmaceutical Studies *I very much recommend this book to a range of audiences, from those wanting an introduction to diagnostic test concepts and methods to active researchers in the area. This book is likely to stimulate considerable progress in development of new statistical methods for diagnostic tests, an area that relative to therapeutics has received little attention from biostatisticians. * Journal of Biopharmaceutical Studies *Table of Contents1. Introduction ; 2. Measures of Accuracy for Binary Tests ; 3. Comparing Binary Tests and Regression Analysis ; 4. The Receiver Operating Characteristic Curve ; 5. Estimating the ROC Curve ; 6. Covariate Effects on Continuous and Ordinal Tests ; 7. Incomplete Data and Imperfect Reference Tests ; 8. Study Design and Hypothesis Testing ; 9. More Topics and Conclusions ; References/Bibliography ; Index

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Book SynopsisDrawing on the author's extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.Trade ReviewThis is an appealing introduction that would be accessible to a variety of students at the college level. Its strengths are clarity and directness with an abundance of good examples and case studies. * MAA Review *Table of ContentsPART I - UNDERSTANDING THE STATISTICS; PART II - ANALYSING EXPERIMENTAL DATA

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Book SynopsisHow strongly should you believe the various propositions that you can express?That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent''s evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: Probability - degrees of belief should be probabilities Calibration - they should be calibrated with evidence Equivocation - they should otherwise equivocate between basic outcomesObjective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failureTable of ContentsPreface ; 1. Introduction ; 2. Objective Bayesianism ; 3. Motivation ; 4. Updating ; 5. Predicate Languages ; 6. Objective Bayesian Nets ; 7. Probabilistic Logic ; 8. Judgement Aggregation ; 9. Languages and Relativity ; 10. Objective Bayesianism in Perspective ; References ; Index

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Book SynopsisProviding a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader''s knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time serTrade Reviewa fascinating read...excellent * CFA Society of the UK *I really recommend this book. It is a very good read and it is very reasonably priced. * Paul Eilers, The Newsletter of the Dutch Classification Society *Table of Contents1. Introduction ; 2. The Local Level Model ; 3. The Local Linear Trend Model ; 4. The Local Level Model with Seasonal ; 5. The Local Level Model with Explanatory Variable ; 6. The Local Level Model with Intervention Variable ; 7. The UK Seat Belt and Inflation Models ; 8. General Treatment of Univariate State Space Models ; 9. Multivariate Time Series Analysis ; 10. State Space and Box-Jenkins Methods for Time Series Analysis ; 11. State Space Modelling in Practice ; 12. Conclusions ; Appendix A UK Drivers KSI and Petrol Price ; Appendix B Road Traffic Fatalities in Norway and Finland ; Appendix C UK Front and Rear Seat Passengers KSI ; Appendix D UK Price Changes

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Book SynopsisThis new edition updates Durbin & Koopman''s important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.Trade ReviewReview from previous edition ...provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis...This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used. * Journal of the Royal Statistical Society *Table of ContentsPART I: THE LINEAR STATE SPACE MODEL; PART II: NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS

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Book SynopsisThis text presents the two complementary aspects of thermal physics as an integrated theory of the properties of matter. Conceptual understanding is promoted by thorough development of basic concepts. In contrast to many texts, statistical mechanics, including discussion of the required probability theory, is presented first. This provides a statistical foundation for the concept of entropy, which is central to thermal physics. A unique feature of the book is the development of entropy based on Boltzmann''s 1877 definition; this avoids contradictions or ad hoc corrections found in other texts. Detailed fundamentals provide a natural grounding for advanced topics, such as black-body radiation and quantum gases. An extensive set of problems (solutions are available for lecturers through the OUP website), many including explicit computations, advance the core content by probing essential concepts. The text is designed for a two-semester undergraduate course but can be adapted for one-semeTrade ReviewIn his innovative new text, Carnegie Mellon University physics professor Robert Swendsen presents the foundations of statistical mechanics with, as he puts it, a detour through thermodynamics. That's a desirable strategy because the statistical approach is more fundamental than the classical thermodynamics approach and has many applications to current research problems. [] The mathematical notation is carefully introduced and useful; the selected mathematical techniques are clearly explained in a conversational style that both graduate and advanced undergraduate students will find easy to follow. The author's subject organization and conceptual viewpoint address some of the shortcomings of conventional developments of thermal physics and will be helpful to students and researchers seeking a deep appreciation of statistical physics. * Physics Today *Bob Swendsen's book is very well thought out, educationally sound, and more original than other texts. * Jan Tobochnik, Kalamazoo College, USA *Robert Swendsen is a well-respected researcher who has developed many novel algorithms that illustrate his deep understanding of statistical mechanics. His textbook reflects his deep understanding and will likely have a major impact on the way statistical mechanics and thermodynamics is taught. Particularly noteworthy is Swendsen's treatment of entropy, following Boltzmann's original definition in terms of probability, and his comprehensive discussion of the fundamental principles and applications of statistical mechanics and thermodynamics. Students and instructors will enjoy reading the book as much as Swendsen obviously enjoyed writing it. * Harvey Gould, Clark University, USA *In this reader-friendly, excellent text, the author provides a unique combination of the best of two worlds: traditional thermodynamics (following Callen's footsteps) and modern statistical mechanics (including VPython codes for simulations). * Royce Zia, Virginia Polytechnic Institute and State University, USA *Swendsen is famous for developing Monte Carlo algorithms which dramatically speed up the simulation of many systems near a phase transition. The ideas for those algorithms required deep understanding of statistical mechanics, an understanding which is now fully applied to this excellent textbook. * Peter Young, University of California, USA *Table of ContentsI ENTROPY; II INTRODUCTION TO THERMODYNAMICS; III CLASSICAL STATISTICAL MECHANICS; IV QUANTUM STATISTICAL MECHANICS

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Book SynopsisThis self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.Trade ReviewUnlike many textbooks in epidemiology, there is no long wordy preamble. The characteristic style is set straight away. The book is also highly successful in presenting a unified approach. What is also striking, is that the authors have managed to say something useful and clear about many of the all too numerous minor problems that are inevitably encountered in practice. In my view this is simply an excellent text. * Andrew Pickles, Institute of Psychiatry, London, Statistical Methods in Medical Research 1994:3 *An excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments. * Australasian Epidemiological Association News, 12/94 *Provides probably the most coherent and logical exposition of the use of statistical models in epidemiology that is currently available ... an excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments. * AEA News 12/94 *Clayton and Hills have filled the gap with an interesting text which is based mainly on probability models and likelihood. This is an unusual approach. but is precisely what is missing in many other textbooks for epidemiologists ... this is an important text for those interested in understanding statistical reasoning in epidemiology. * Maria Blettner, International Journal of Epidemiology *The authors have produced a text that will be extremely valuable to those teaching epidemiologic methods... Statistical Models in Epidemiology courageously cuts new paths into the traditional epidemiologic approach to statistical training. * Journal of the American Statistics Association *This book gives some very clear explanations ... Each point is well illustrated with small examples and there are exercises throughout. It is pleasing to see full solution to all the exercises. * Public Health (1994) 108 *Table of ContentsI. PROBABILITY MODELS AND LIKELIHOOD; II. REGRESSION MODELS; III. APPENDICES

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Book SynopsisThis second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.Trade ReviewThe book is readable, well-written, and amply illustrated * Technometrics, August 1995 (previous edition) *It belongs in the possession of every statistician who encouters longitudinal data. * Journal of the American Statistical Association *. . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research. * Statistics in Medicine *The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful. * Pharmaceutical Statistics *The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated. * Pharmaceutical Statistics *Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data. * Zentralblatt MATH *Table of Contents1. Introduction ; 2. Design considerations ; 3. Exploring longitudinal data ; 4. General linear models ; 5. Parametric models for covariance structure ; 6. Analysis of variance methods ; 7. Generalized linear models for longitudinal data ; 8. Marginal models ; 9. Random effects models ; 10. Transition models ; 11. Likelihood-based methods for categorical data ; 12. Time-dependent covariates ; 13. Missing values in longitudinal data ; 14. Additional topics ; Appendix ; Bibliography ; Index

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Book SynopsisPublished in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasized the interaction between geophysicists, geologists, and engineers, and was received well by professionals, academics, and both graduate and undergraduate students.In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics. Key geostatistical concepts such as integration of production data, scale-up, and cosimulation receive greater attention, and new topics like model checking, multiple point simulation, and production data integration are included in detail. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples. A greater number of examples also are included, such as the integration oTable of Contents1. Introduction ; 2. Modeling Principles ; 3. Modeling Prerequisites ; 4. Modeling Methods ; 5. Model Applications ; 6. Special Topics ; Glossary and Notation ; Bibliography ; Index

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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