By Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
Bayesian lacking information difficulties: EM, information Augmentation and Noniterative Computation provides recommendations to lacking information difficulties via particular or noniterative sampling calculation of Bayesian posteriors. The tools are according to the inverse Bayes formulae stumbled on through one of many writer in 1995. employing the Bayesian method of vital real-world difficulties, the authors concentrate on certain numerical suggestions, a conditional sampling procedure through facts augmentation, and a noniterative sampling strategy through EM-type algorithms.
After introducing the lacking info difficulties, Bayesian technique, and posterior computation, the ebook succinctly describes EM-type algorithms, Monte Carlo simulation, numerical recommendations, and optimization equipment. It then offers distinct posterior ideas for difficulties, akin to nonresponses in surveys and cross-over trials with lacking values. It additionally presents noniterative posterior sampling ideas for difficulties, comparable to contingency tables with supplemental margins, aggregated responses in surveys, zero-inflated Poisson, capture-recapture types, combined results types, right-censored regression version, and limited parameter types. The textual content concludes with a dialogue on compatibility, a basic factor in Bayesian inference.
This publication bargains a unified therapy of an array of statistical difficulties that contain lacking info and restricted parameters. It exhibits how Bayesian systems might be important in fixing those problems.
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Additional resources 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