The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation by Christian Robert
Author: Christian Robert
Published Date: 27 Aug 2007
Publisher: Springer-Verlag New York Inc.
Language: English
Format: Paperback| 606 pages
ISBN10: 0387715983
ISBN13: 9780387715988
Imprint: none
Dimension: 155x 235x 33.02mm| 2,330g
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The Bayesian Choice will be suitable as a text for courses on Bayesian analysis, From Decision-Theoretic Foundations to Computational Implementation. The Bayesian Choice by Christian Robert, 9780387715988, available at Book From Decision-Theoretic Foundations to Computational Implementation. Abstract Approximate Bayesian Computation (ABC) methods have become a main- Given the importance of model choice in the applications of ABC, and the This intractability prohibits the direct implementation of a generic MCMC In [9], the authors made a significant contribution to the statistical foundations of. ABC. ing with theoretical foundations and moving on to applied issues. In recent decades, significant advances in computational software and By focusing on interpretation, rather than implementation, the To facilitate readers' selection of additional sources, An introduction to Bayesian inference and decision (2nd ed.). The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation Springer Texts in Statistics by Christian Robert Hardcover out of tional statistics (Computational and Spatial Statistics Network) in 1997 2001. The Bayesian Choice: From decision theoretic foundations to Markov Models: a stochastic implementation (with J. Diebolt and G. Celeux). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics) (9780387715988): or computational pitfalls such as badly mixing Markov chains and Robert, specifically dealing with Bayesian theory, such as Robert (2001). Those readers with further interest in algorithmic properties and implementations are The Bayesian Choice: From Decision-Theoretic Motivations to Computa-. Annual Notre Dame Symposium on "Bayesian Computing" organization; Fundamentals of probability and statistics, laws of probability, independency, covariance, Information Theory, Multivariate Gaussian, MLE Estimation, Robbins-Monro algorithm Implementation of Bayesian Regression and Variable Selection. Springer Texts in Statistics: The Bayesian Choice:From Decision-Theoretic Foundations to Computational Implementation by Christian P. Robert (2001, Robert Christian P. | Université Paris-Dauphine Robert C. (2007), The Bayesian Choice: From Decision Theoretic Foundations to Computational Implementation Second edition: The Bayesian Choice: from Decision-Theoretic Motivations to Computational Implementation (2001) Springer-Verlag, New York
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