By Ming T. Tan
Bayesian lacking facts difficulties: EM, info Augmentation and Noniterative Computation provides recommendations to lacking facts difficulties via particular or noniterative sampling calculation of Bayesian posteriors. The tools are in response to the inverse Bayes formulae came across by way of one of many writer in 1995. utilising the Bayesian method of vital real-world difficulties, the authors specialise in precise numerical options, a conditional sampling technique through info augmentation, and a noniterative sampling process through EM-type algorithms.
After introducing the lacking info difficulties, Bayesian procedure, and posterior computation, the e-book succinctly describes EM-type algorithms, Monte Carlo simulation, numerical strategies, and optimization tools. It then offers particular posterior suggestions for difficulties, corresponding to nonresponses in surveys and cross-over trials with lacking values. It additionally presents noniterative posterior sampling strategies for difficulties, equivalent 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 bargains a unified remedy of an array of statistical difficulties that contain lacking information and limited parameters. It exhibits how Bayesian methods might be priceless in fixing those problems.
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