Titlebook: Bayesian Core: A Practical Approach to Computational Bayesian Statistics; Jean-Michel Marin,Christian P. Robert Textbook 20071st edition S

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Textbook 20071st editions in all ?elds, given the versatility of the Bayesian tools. It can also be used for a more classical statistics audience when aimed at teaching a quick entry to Bayesian statistics at the end of an undergraduate program for instance. (Obviously, it can supplement another textbook on data analysis a

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Svetoslav Danchev,Grigoris Pavlou concept of “variable dimension models,” where the structure (dimension) of the model is determined a posteriori using the data. This opens new perspectives for Bayesian inference such as model averaging but calls for a special simulation algorithm called reversible jump MCMC.

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Mixture Models,known distributions. This representation is naturally called a mixture of distributions. Inference about the parameters of the elements of the mixtures and the weights is called mixture estimation, while recovery of the original distribution of each observation is called classification (or, more exa