Bayesian Data Analysis - Gelman, Andrew (Department of Statistics, Columbia University, New Y; Carlin, John B. (The Royal Children's Hospital, Parkville, Victoria,; Stern, Hal S. (University of California, Irvine, USA)
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Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo,…mehr

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Produktbeschreibung
Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation. Data sets and other materials are available online.
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Autorenporträt
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Doanld B. Rubin