Bayesian Essentials with R (Springer Texts in Statistics) 2nd Edition by Jean-Michel Marin | (PDF) Free Download

Description

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. 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. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I am an avid reader of the author’s blog and have also have also found his book on Monte Carlo simulation to be very helpful. I bought this book primarily for its chapter on time series. However, I was sorely disappointed.The conversion of the book to Kindle format is a mess. Formulae are haphazardly either scanned or converted to”flow” text. This leads to inconsistent use of fonts which makes reading this book an annoying affair. Subscripts are often detached from the main variable letter. The flow-style text makes reading the r-code difficult. I have read the author’s reviews of other books and he has been scathing of texts that were subject to far lesser crimes. I doubt he was involved in converting this book to Kindle format. I am sure he would be embarrassed by this mess.As for the substance of the time series chapter, the book suggests that the data should determine whether the time series is stationary, and that imposing stationarity restrictions on the model is at best artificial, particularly in the context of Bayesian modeling. Despite this, the balance of the chapter focuses on the difficult problem of building stationarity restrictions into the Bayesian model. This approach does not seem to be consistent with other texts on implementing bayesian models for time series and I was left wondering whether the stationarity restriction approach is good practice in the context of a bayesian model especially since it makes the model so much more complicated.If you are looking for an introductory book on Bayesian modeling, I would recommend The Bugs Book, Kruschke’s book, Gelman’s book on hierarchical modeling or Barber’s Bayesian Reasoning. Congdon is also good. Barber also has an entire book dedicated to Bayesian time series, but this is pretty advanced.

⭐The authors provide a succinct (and occasionally witty) exposition of useful statistical methods and programming techniques.

⭐I praise what is a very substantial, carefully written and even visually appealing book, which, with its rigorous approach, a wide array of end-of-chapter exercises, and coverage of important topics like spatial- and time-series analysis, may be the best candidate for a textbook in a graduate course on Bayesian statistics.At the same time – and I am sorry, I know that this is not constructive or specific criticism, but I am not an editor or peer reviewer, and do not have the time or the expertise to spell out the suggested alternatives – oh boy, do I *not* feel that this is a helpful textbook that makes it easy on the student. From page 33’s what-I-suppose-is-introduction-to-the-Bayesian-method to the chapter on time series – which, before it came to HMMs, took a very unexpected direction – I often wondered “Why choose this material, and put it here?”Why did Andrew Gelman (according to the preface) recommend title “Bayesian essentials” for a technically pretty demanding – sometimes, like in the chapter on the linear regression, unnecessarily demanding – book that does not really do the essentials well, but goes well further afield? Why did the authors choose to avoid BUGS? (There’s R code in the book, but it’s, again, not what you would expect).To sum it up, I did not get “Bayesian essentials”. The book may well have a bright future as a textbook, but if you are looking for real “Bayesian essentials”, John Kruschke’s “Doing Bayesian data analysis” is an excellent, one-of-its-kind choice.

⭐This is a revised version of the authors’ book Bayesian Core, which has been made a little bit more accessible. The maths has been toned down and more R code has been put in. Very interesting material and very well presented,

⭐Je n’ai lu et fait les exercices que jusqu’à la moitié du livre environ. Les explications pourraient être plus claires, et les solutions des exercices également. D’innombrables erreurs/coquilles panachent le livre. J’en ai même signalé certaines aux auteurs, sans réponse de leur part. Bref, passez votre chemin.

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