There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of... powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over... theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike,... the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book’s website includes solutions to the problems, data sets, software advice, and other ancillary material.

Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple... decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.

A new book in the Econometric Exercises series, this volume contains questions and answers to provide students with useful practice, as they attempt to... master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises.

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents... statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental website.

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of... interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in... social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings.New to the Second EditionTwo chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical modelsMore technical and philosophical details on prior distributionsA dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervalsRequiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.

Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and... applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling—now commonplace in Bayesian data analysis A new chapter on Bayesian design that emphasizes Bayesian clinical trials A completely revised and expanded section on ranking and histogram estimation A new case study on infectious disease modeling and the 1918 flu epidemic A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem—available both electronically and in print Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial... applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas.... The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.The second edition:Provides an integrated presentation of theory, examples, applications and computer algorithms.Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.Praise for the First Edition:“It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews“This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics“The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

Now available for the first time, "one of the most secret documents of the Cold War" (New York Times): the government's own report of the Bay of Pigs... fiasco. For decades, the CIA's top secret postmortem on the April 1961 Bay of Pigs invasion has been the holy grail of historians, students, and survivors of the failed invasion of Cuba. But the scathing internal report on the worst foreign policy debacle of the Kennedy administration, written by the CIA's then-Inspector General Lyman Kirkpatrick, has remained tightly guarded--until now. Dislodged from the government through the Freedom of Information Act, here is an uncompromising look at high officials' arrogance, ignorance, and incompetence, as displayed in their attitude toward Castro's revolution and toward the Cuban exiles the CIA had organized to invade the island. Including the complete report and a wealth of supplementary materials, Bay of Pigs Declassified provides a fascinating picture of the operation and of the secret world of the espionage establishment, with stories of plots, counterplots, and intra-agency power struggles worthy of a Le Carr novel. Peter Kornbluh directs the Cuba Documentation Project at the National Security Archive. The Archive serves scholars, journalists, Congress, public interest organizations, and citizens by obtaining and disseminating internal U.S. government documentation that is indispensable for informed public debate. Includes: the complete text of the CIA report; a critical introduction; the newly declassified response to the report from Richard Bissell, who masterminded the operation; the first joint interview with the managers of the invasion, Jacob Esterline and Colonel Jack Hawkins; a comprehensive chronology; and biographies of the key participants.

The Cajun coast of Louisiana is home to a way of life as unique, complex, and beautiful as the terrain itself.  As award-winning travel writer Mike... Tidwell journeys through the bayou, he introduces us to the food and the language, the shrimp fisherman, the Houma Indians, and the rich cultural history that makes it unlike any other place in the world. But seeing the skeletons of oak trees killed by the salinity of the groundwater, and whole cemeteries sinking into swampland and out of sight, Tidwell also explains why each introduction may be a farewell—as the storied Louisiana coast steadily erodes into the Gulf of Mexico.Part travelogue, part environmental exposé, Bayou Farewell is the richly evocative chronicle of the author's travels through a world that is vanishing before our eyes.

In the decade prior to the publication of her landmark novel, The Awakening (1899), Kate Chopin wrote about ninety short stories. She gathered... twenty-three of them in a collection entitled Bayou Folk in 1894, and followed that three years later with a collection of twenty-one more in A Night in Acadie. Together, these nuanced portraits of nineteenth-century inhabitants of New Orleans and Natchitoches Parish exquisitely form a sort of Southern novel of manners.Chopin was deeply influenced by the work of French and American realists. Many of the stories in Bayou Folk concern young people seeking good marriage partners and better lives for themselves. Expanding this theme into a search for balance and harmony, personal fulfillment, and cultural richness, A Night in Acadie is, Bernard Koloski notes in his Introduction, "one of America's best nineteenth-century collections of short stories -- and one of the most compassionate views of life in American realistic fiction". With a gentle, knowing gaze, Chopin evoked the distant world of Louisiana plantations and 'Cadian balls, and anticipated the thoroughly modern multi-ethnic, gender-sensitive, and sexually charged world of our century.