By Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
ISBN-10: 142007749X
ISBN-13: 9781420077490
Bayesian lacking information difficulties: EM, facts Augmentation and Noniterative Computation provides recommendations to lacking info difficulties via specific or noniterative sampling calculation of Bayesian posteriors. The tools are according to the inverse Bayes formulae came upon by way of one of many writer in 1995. making use of the Bayesian method of very important real-world difficulties, the authors specialise in targeted numerical recommendations, a conditional sampling strategy through facts augmentation, and a noniterative sampling procedure through EM-type algorithms.
After introducing the lacking information difficulties, Bayesian technique, and posterior computation, the ebook succinctly describes EM-type algorithms, Monte Carlo simulation, numerical options, and optimization tools. It then supplies precise posterior suggestions for difficulties, equivalent to nonresponses in surveys and cross-over trials with lacking values. It additionally offers noniterative posterior sampling recommendations for difficulties, resembling contingency tables with supplemental margins, aggregated responses in surveys, zero-inflated Poisson, capture-recapture versions, combined results versions, right-censored regression version, and limited parameter types. The textual content concludes with a dialogue on compatibility, a basic factor in Bayesian inference.
This booklet deals a unified therapy of an array of statistical difficulties that contain lacking information and limited parameters. It indicates how Bayesian methods will be beneficial in fixing those problems.
Read Online or Download Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation (Chapman & Hall/CRC Biostatistics Series) PDF
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Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation (Chapman & Hall/CRC Biostatistics Series) by Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
by Anthony
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