Recently there have been a wealth of good books published on Bayesian methods and the Markov chain Monte Carlo approach to its implementation. For the beginner Berry's introductory book is a good place to start. Bernardo and Smith are experts in the field who have participated in many of the Bayesian conferences held in Valencia and much of that lterature is contained in this book. They originally wrote the book in 1993 (with...
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An excellent book. Three things I like: (1) it is correct (so many others are not), (2) it can be read by someone who does not have a PhD in math, (3) they don't pull punches. Appendix B explains directly why all alternative theories are nonsense.
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This is an extremely nice introduction to Bayesian statistical methods. It takes you from the very basics - even who Thomas Bayes was (who happens to be buried in Bunhill Fields cemetery in London with William Blake (Songs of Innocence and Experience, Jerusalem), Daniel Defoe (Robinson Crusoe), John Bunyan (Pilgrim's Progress)). Its chapters are divided into sections forming an Introduction, Foundations, Generalizations,...
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This book provides a thorough introduction to Bayesian theory and decision analysis. It presents a coherent defense of the subjective view of probability that is driving many new technologies, including probabilistic graphical models, data mining, information retrieval and machine learning, as well as, classical problems such as control and econometrics. It is therefore a must for students and practitioners in these fields...
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This excellent book presents the foundations of the Bayesian approach to uncertainty in systematic way. Statistical inference is treated as a decision problem which, the authors argue, should be solved on the basis of a subjective probability measure. The emphasis is on ideas rather than technical details and every chapter ends with a detailed discussion of specially important subjects. The list of references is so comprehensive...
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