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.

Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics)

Computers & Technology

Christian P. Robert

Jean-Michel Marin

Computer Science

Springer

The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics)

This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.

Science & Math

Mathematics

Springer-Verlag New York

Monte Carlo Statistical Methods (Springer Texts in Statistics)

Provides an introduction to Markov Chains and covers the basics of random variable generation and Monte Carlo integration. Demonstrates how MCMCmethods apply to some statistical settings that can't be processed otherwise. DLC: Mathematical statistics.

Monte Carlo Statistical Methods

Springer, Verlag

This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.

Introducing Monte Carlo Methods with R (Use R!)

George Casella

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Cet ouvrage couvre l'approche dite bayésienne de l'inférence statistique et en particulier ses aspects décisionnels. Les bases de cette axiomatique (choix de l'a priori, décisions optimales, tests et régions de confiance) sont abordées en détail, ainsi que des ouvertures plus récentes de l'analyse bayésienne comme le choix de modèles, l'utilisation de méthodes numériques stochastiques d'approximation (MCMC), la théorie des lois non informatives (axiomes de Berger-Bernardo) et la relation à la théorie classique de l'admissibilité. Chaque chapitre est complété par une suite extensive d'exercices de difficulté croissante et par des notes bibliographiques sur les thèmes abordés. La version anglaise de cet ouvrage, The Bayesian Choice, a été publiée en 2001 par Springer New York. L’auteur a obtenu le Prix DeGroot 2004 décerné par l'International Society for Bayesian Analysis. Le comité de sélection a estimé que : « Le livre de Christian Robert établit un nouveau standard moderne de livre de référence sur le thème des méthodes bayésiennes, en particulier celles utilisant les techniques MCMC, ce qui place l’auteur en digne successeur des écrits de DeGroot et Berger ».

Le choix bayésien: Principes et pratique (Statistique et probabilités appliquées) (French Edition)

Business & Money

Management & Leadership

Monte Carlo Statistical Methods - 2nd Edition

World Book

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Books by Christian P. Robert