Introduction to Bayesian Statistics

Author: William M. Bolstad,James M. Curran

Publisher: John Wiley & Sons

ISBN: 1118593227

Category: Mathematics

Page: 624

View: 6339

"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.
Posted in Mathematics

Introduction to Bayesian Statistics

Author: Karl-Rudolf Koch

Publisher: Springer Science & Business Media

ISBN: 3540727264

Category: Science

Page: 249

View: 2549

This book presents Bayes’ theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters. It does so in a simple manner that is easy to comprehend. The book compares traditional and Bayesian methods with the rules of probability presented in a logical way allowing an intuitive understanding of random variables and their probability distributions to be formed.
Posted in Science

Bayesian statistics

principles, models, and applications

Author: S. James Press

Publisher: John Wiley & Sons Inc


Category: Mathematics

Page: 237

View: 7342

An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems. First part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. Second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. Includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay. Contains exercises.
Posted in Mathematics

Bayesian Statistics

An Introduction

Author: Peter M. Lee

Publisher: John Wiley & Sons

ISBN: 1118359771

Category: Mathematics

Page: 488

View: 7906

Bayesian Statistics is the school of thought that combines priorbeliefs with the likelihood of a hypothesis to arrive at posteriorbeliefs. The first edition of Peter Lee’s book appeared in1989, but the subject has moved ever onwards, with increasingemphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such asvariational methods, Bayesian importance sampling, approximateBayesian computation and Reversible Jump Markov Chain Monte Carlo(RJMCMC), providing a concise account of the way in which theBayesian approach to statistics develops as well as how itcontrasts with the conventional approach. The theory is built upstep by step, and important notions such as sufficiency are broughtout of a discussion of the salient features of specificexamples. This edition: Includes expanded coverage of Gibbs sampling, including morenumerical examples and treatments of OpenBUGS, R2WinBUGS andR2OpenBUGS. Presents significant new material on recent techniques such asBayesian importance sampling, variational Bayes, ApproximateBayesian Computation (ABC) and Reversible Jump Markov Chain MonteCarlo (RJMCMC). Provides extensive examples throughout the book to complementthe theory presented. Accompanied by a supporting website featuring new material andsolutions. More and more students are realizing that they need to learnBayesian statistics to meet their academic and professional goals.This book is best suited for use as a main text in courses onBayesian statistics for third and fourth year undergraduates andpostgraduate students.
Posted in Mathematics

Think Bayes

Author: Allen Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1491945443

Category: Computers

Page: 210

View: 2234

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
Posted in Computers

A Student’s Guide to Bayesian Statistics

Author: Ben Lambert

Publisher: SAGE

ISBN: 1526418266

Category: Social Science

Page: 520

View: 4849

Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes' rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.
Posted in Social Science

Bayesian Statistics 3

Proceedings of the Third Valencia International Meeting, June 1-5, 1987

Author: J. M. Bernardo

Publisher: Oxford University Press, USA


Category: Science

Page: 805

View: 6801

The field of statistics has undergone rapid and wide development during the past two decades, and the Bayesian approach to statistics has provided both a general framework and a creative stimulus for all aspects of this development. This volume describes the work presented at the Third Valencia International Meeting on Bayesian Statistics, the main source of information and communication about the current state of knowledge and research in Bayesian statistics throughout the world. The research presented--which encompasses both invited papers and selected contributed papers-- has had a profound effect on the foundations of statistical inference and probability, statistical theory and methodology, and the applications of statistics in science, technology, medicine, business, law, and public policy. The contributors to this volume form a virtual Who's Who in the area of Bayesian statistics.
Posted in Science

Bayesian Statistics and Marketing

Author: Peter E. Rossi,Greg M. Allenby,Rob McCulloch

Publisher: John Wiley & Sons

ISBN: 0470863684

Category: Mathematics

Page: 368

View: 6883

The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods. Written by the leading experts in the field, this unique book: Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models. Provides a self-contained introduction to Bayesian methods. Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems. Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies. Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.
Posted in Mathematics

Bayesian Statistics for the Social Sciences

Author: David Kaplan

Publisher: Guilford Publications

ISBN: 1462516513

Category: Psychology

Page: 318

View: 1573

Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.
Posted in Psychology

Bayesian Statistics and Its Applications

Author: Satyanshu K. Upadhyay,Umesh Singh,Dipak Dey

Publisher: Anshan Pub

ISBN: 9781905740000

Category: Mathematics

Page: 507

View: 3032

In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.
Posted in Mathematics

Bayesian Statistical Inference

Author: Gudmund R. Iversen

Publisher: SAGE

ISBN: 9780803923287

Category: Mathematics

Page: 80

View: 1482

Empirical researchers, for whom Iversen's volume provides an introduction, have generally lacked a grounding in the methodology of Bayesian inference. As a result, applications are few. After outlining the limitations of classical statistical inference, the author proceeds through a simple example to explain Bayes' theorem and how it may overcome these limitations. Typical Bayesian applications are shown, together with the strengths and weaknesses of the Bayesian approach. This monograph thus serves as a companion volume for Henkel's Tests of Significance (QASS vol 4).
Posted in Mathematics

Bayesian Statistics, A Review

Author: D. V. Lindley

Publisher: SIAM

ISBN: 9780898710021

Category: Mathematics

Page: 83

View: 1946

A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics.
Posted in Mathematics

Bayesian Statistics 2

Proceedings of the Second Valencia International Meeting, September 6/10, 1983

Author: J. M. Bernardo

Publisher: North Holland


Category: Mathematics

Page: 778

View: 5092

Providing a comprehensive overview of recent research in Bayesian Statistics, this book includes contributions from most of the leading experts in the field. It covers a broad range of topics, from foundational philosophy to practical case studies.
Posted in Mathematics

A First Course in Bayesian Statistical Methods

Author: Peter D. Hoff

Publisher: Springer Science & Business Media

ISBN: 9780387924076

Category: Mathematics

Page: 272

View: 9296

A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
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Bayesian Statistics 9

Author: José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman

Publisher: Oxford University Press

ISBN: 0199694583

Category: Mathematics

Page: 706

View: 3365

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Posted in Mathematics

Bayesian Statistics 6

Proceedings of the Sixth Valencia International Meeting

Author: J. M. Bernardo

Publisher: Oxford University Press

ISBN: 9780198504856

Category: Business & Economics

Page: 867

View: 4659

Bayesian statistics is a dynamic and fast-growing area of statistical research, and the Valencia International Meetings, held every four years, provide the main forum for discussion of developments in the field. The resulting Proceedings form a definitive and up-to-date collection of research. This sixth volume will be an indispensable reference for all researchers in statistics.
Posted in Business & Economics

Bayesian Core: A Practical Approach to Computational Bayesian Statistics

Author: Jean-Michel Marin,Christian Robert

Publisher: Springer Science & Business Media

ISBN: 0387389830

Category: Mathematics

Page: 258

View: 594

This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.
Posted in Mathematics

Applied Bayesian Statistics

With R and OpenBUGS Examples

Author: Mary Kathryn Cowles

Publisher: Springer Science & Business Media

ISBN: 1461456967

Category: Mathematics

Page: 232

View: 2295

This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Kate Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.
Posted in Mathematics

Elements of Bayesian Statistics

Author: Florens

Publisher: CRC Press

ISBN: 9780824781231

Category: Mathematics

Page: 544

View: 2210

The ingratiating title notwithstanding, this is in no standard sense a text but a monograph, based largely upon the authors' research over a period of years, and intended to be read by sophisticated students of theoretical statistics. No exercises attach to the nine chapters, nor are they interrup
Posted in Mathematics

Multivariate Bayesian Statistics

Models for Source Separation and Signal Unmixing

Author: Daniel B. Rowe

Publisher: CRC Press

ISBN: 1420035266

Category: Mathematics

Page: 352

View: 7384

Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them. Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the "cocktail-party" analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters. Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many "cocktail party" problems they may confront in practice.
Posted in Mathematics