By Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
Bayesian lacking information difficulties: EM, facts Augmentation and Noniterative Computation provides strategies to lacking information difficulties via particular or noniterative sampling calculation of Bayesian posteriors. The tools are in response to the inverse Bayes formulae stumbled on via one of many writer in 1995. using the Bayesian method of vital real-world difficulties, the authors specialize in targeted numerical strategies, a conditional sampling technique through info augmentation, and a noniterative sampling procedure through EM-type algorithms.
After introducing the lacking facts difficulties, Bayesian method, and posterior computation, the e-book succinctly describes EM-type algorithms, Monte Carlo simulation, numerical strategies, and optimization equipment. It then supplies unique posterior ideas for difficulties, similar 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 types, combined results versions, right-censored regression version, and limited parameter types. The textual content concludes with a dialogue on compatibility, a primary factor in Bayesian inference.
This publication bargains a unified remedy of an array of statistical difficulties that contain lacking information and limited parameters. It indicates how Bayesian methods should be valuable in fixing those problems.
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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