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 unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy.
It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.
The two core functions map and map2stan of this package allow a variety of statistical models to be constructed from standard model formulas. 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. 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.
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience.
The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance ANOVA and comparisons in ANOVA, multiple regression, and chi-square contingency table analysis.
This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.
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.
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 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.
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. 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. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples.
The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference. Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations.
Its unique features include thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis. Bayesian Data Analysis offers the practicing statistician singular guidance on all aspects of the subject.
Bayesian data analysis is concerned with the type of data manipulations, transformations, and just plain playing with the data, that any serious scientist engages in during the statistical or other analysis of his data. It is largely a post-data procedure, rather than a pre-data procedure, since even when it is desirable to think through such matters quite carefully prior to obtaining the data, in many real world experiments time and other constraints would provide limits on such activities.
Compare Hacking or the discussion in Hodges concerning how much is enough. Bayesian data analysis goes beyond the mere data manipulations, however, and attempts to integrate the theory of subjective probability with such data analysis. In this respect it differs from other data-analytic approaches, which appear, more or less, to abandon probability.
In this article the author attempts further to elucidate the theory of Bayesian data analysis begun in Hill. This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.
The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature.
The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages. Carlin Andrew Gelman Hal S. Stern, Donald B. This is great news. I strongly prefer pdfs because they fit on my iPad and are searchable. The iPad changed my reading life, especially after they improved resolution so that subscripts and even double subscripts are readable.
My first edition copy had something lik 64 pages upside down and backwards! You get a lot of credit for this pdf release. The immediate motivation was when we were working on our Bayesian Workflow book last year and I wrote a chapter talking about BDA3. It may take up to minutes before you receive it. The file will be sent to your Kindle account.
It may takes up to minutes before you received it. Please note : you need to verify every book you want to send to your Kindle. Check your mailbox for the verification email from Amazon Kindle.
Related Booklists. Post a Review To post a review, please sign in or sign up. You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read.
0コメント