By Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
Bayesian lacking information difficulties: EM, information Augmentation and Noniterative Computation offers recommendations to lacking information difficulties via specific or noniterative sampling calculation of Bayesian posteriors. The equipment are according to the inverse Bayes formulae stumbled on by means of one of many writer in 1995. utilizing the Bayesian method of vital real-world difficulties, the authors specialise in distinct numerical suggestions, a conditional sampling process through info augmentation, and a noniterative sampling strategy through EM-type algorithms.
After introducing the lacking info difficulties, Bayesian process, and posterior computation, the booklet succinctly describes EM-type algorithms, Monte Carlo simulation, numerical ideas, and optimization equipment. It then provides precise posterior suggestions for difficulties, resembling nonresponses in surveys and cross-over trials with lacking values. It additionally offers noniterative posterior sampling options for difficulties, comparable to contingency tables with supplemental margins, aggregated responses in surveys, zero-inflated Poisson, capture-recapture versions, combined results versions, right-censored regression version, and restricted parameter versions. The textual content concludes with a dialogue on compatibility, a primary factor in Bayesian inference.
This ebook deals a unified remedy of an array of statistical difficulties that contain lacking information and restricted parameters. It indicates how Bayesian tactics could be worthy in fixing those problems.
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Extra 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