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(More customer reviews)I have used 'Bayesian Core' to teach Bayesian statistics to a class of Masters students majoring in finance, statistics or business with an undergraduate mathematics/statistics background and found it to be quite a good book for this purpose. I would have to disagree though with the previous reviewer (A.L.H Mayne) and suggest that this book could also be good for a practitioner, provided they have some prior statistical and mathematical understanding. A limitation for self guided study is the absence of solutions to exercises or hints necessary to ensure understanding for some of the exercises. I would also have to disagree with the previous reviewer over what is or what is not discussed in the book! The statement "conjugacy is mentioned in exercise 2.10 on page 22 with no discussion" is simply not true, the entire page preceding this exercise discusses conjugate priors in particular and they are subsequently used and outlined in the following chapters. There is also considerable attention paid in the book to the use of improper priors, in particular with respect to the implications of using improper priors for the estimation of Bayes factors (Chapter 3). A more thorough description of Jeffrey's Lindley paradox could be provided but a more complete discussion of this seems outside the scope of the book. Similarly, outlining the "historical antecedents ..." and more "...theory" about Jeffrey's prior than what is already provided, while interesting and important in its own right, is not necessarily mandatory reading for the student or practitioner who seeks a practical introduction to the Bayesian approach. To highlight "The implementation of the Monte Carlo method is straightforward" without adding the next few words used in the book "at least on a formal basis" seems terribly insincere and pedantic.
What the book is? The book presents a Bayesian approach to the analysis of topics commonly analysed in statistics designed to allow the reader to quickly grasp the essential elements of Bayesian principles and to put this into practice with examples using R code (simple computing syntax) provided on the website. There are surely limitations of this approach (albeit also acknowledged by the authors!) namely a less than full treatment of topics and of theoretical derivations for approaches. Some of the exercises are also difficult and these exercises could well do with hints being provided or a short statistical annotation.
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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.
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