By Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
Bayesian lacking information difficulties: EM, facts Augmentation and Noniterative Computation offers recommendations to lacking info difficulties via specific or noniterative sampling calculation of Bayesian posteriors. The tools are according to the inverse Bayes formulae came across by means of one of many writer in 1995. employing the Bayesian method of vital real-world difficulties, the authors concentrate on special numerical strategies, a conditional sampling procedure through facts augmentation, and a noniterative sampling strategy through EM-type algorithms.
After introducing the lacking facts difficulties, Bayesian technique, and posterior computation, the booklet succinctly describes EM-type algorithms, Monte Carlo simulation, numerical thoughts, and optimization equipment. It then offers certain posterior suggestions for difficulties, resembling nonresponses in surveys and cross-over trials with lacking values. It additionally offers noniterative posterior sampling recommendations for difficulties, similar to contingency tables with supplemental margins, aggregated responses in surveys, zero-inflated Poisson, capture-recapture types, combined results versions, right-censored regression version, and restricted parameter types. The textual content concludes with a dialogue on compatibility, a primary factor in Bayesian inference.
This publication bargains a unified therapy of an array of statistical difficulties that contain lacking facts and restricted parameters. It indicates how Bayesian strategies might be worthwhile in fixing those problems.
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Extra info for Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation (Chapman & Hall/CRC Biostatistics Series)
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