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Bayesian Core: A Practical Approach to Computational Bayesian Statistics [electronic resource] / by Jean-Michel Marin, Christian P. Robert.

By: Marin, Jean-Michel [author.].
Contributor(s): Robert, Christian P [author.] | SpringerLink (Online service).
Material type: TextTextSeries: Springer Texts in Statistics: Publisher: New York, NY : Springer New York, 2007Description: XIV, 258 p. 80 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387389837.Subject(s): Distribution (Probability theory | Mathematical statistics | Computer science | Computer simulation | Engineering | Computer science -- Mathematics | Probability Theory and Stochastic Processes | Statistical Theory and Methods | Probability and Statistics in Computer Science | Simulation and Modeling | Computational Intelligence | Computational Mathematics and Numerical AnalysisAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.2 Online resources: Click here to access online
Contents:
User’s Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture–Recapture Experiments -- Mixture Models -- Dynamic Models -- Image Analysis.
In: Springer eBooksSummary: 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. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language. The Bayesian Core can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It serves as a unique textbook for a service course for scientists aiming at analyzing data the Bayesian way as well as an introductory course on Bayesian statistics. The prerequisites for the book are a basic knowledge of probability theory and of statistics. Methodological and data-based exercises are included within the main text and students are expected to solve them as they read the book. Those exercises can obviously serve as assignments, as was done in the above courses. Datasets, R codes and course slides all are available on the book website. Jean-Michel Marin is currently senior researcher at INRIA, the French Computer Science research institute, and located at Université Paris-Sud, Orsay. He has previously been Assistant Professor at Université Paris Dauphine for four years. He has written numerous papers on Bayesian methodology and computing, and is currently a member of the council of the French Statistical Society. Christian Robert is Professor of Statistics at Université Paris Dauphine and Head of the Statistics Research Laboratory at CREST-INSEE, Paris. He has written over a hundred papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow and member of the council of the Institute of Mathematical Statistics, and a Fellow and member of the research committee of the Royal Statistical Society. He is currently co-editor of the Journal of the Royal Statistical Society, Series B, after taking part in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also the winner of the Young Statistician prize of the Paris Statistical Society in 1996 and a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006.
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User’s Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture–Recapture Experiments -- Mixture Models -- Dynamic Models -- Image Analysis.

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. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language. The Bayesian Core can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It serves as a unique textbook for a service course for scientists aiming at analyzing data the Bayesian way as well as an introductory course on Bayesian statistics. The prerequisites for the book are a basic knowledge of probability theory and of statistics. Methodological and data-based exercises are included within the main text and students are expected to solve them as they read the book. Those exercises can obviously serve as assignments, as was done in the above courses. Datasets, R codes and course slides all are available on the book website. Jean-Michel Marin is currently senior researcher at INRIA, the French Computer Science research institute, and located at Université Paris-Sud, Orsay. He has previously been Assistant Professor at Université Paris Dauphine for four years. He has written numerous papers on Bayesian methodology and computing, and is currently a member of the council of the French Statistical Society. Christian Robert is Professor of Statistics at Université Paris Dauphine and Head of the Statistics Research Laboratory at CREST-INSEE, Paris. He has written over a hundred papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow and member of the council of the Institute of Mathematical Statistics, and a Fellow and member of the research committee of the Royal Statistical Society. He is currently co-editor of the Journal of the Royal Statistical Society, Series B, after taking part in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also the winner of the Young Statistician prize of the Paris Statistical Society in 1996 and a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006.

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