By Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
Bayesian lacking information difficulties: EM, facts Augmentation and Noniterative Computation provides ideas to lacking info difficulties via particular or noniterative sampling calculation of Bayesian posteriors. The equipment are in accordance with the inverse Bayes formulae came upon by way of one of many writer in 1995. employing the Bayesian method of very important real-world difficulties, the authors specialise in distinctive numerical ideas, a conditional sampling procedure through info augmentation, and a noniterative sampling strategy through EM-type algorithms.
After introducing the lacking facts difficulties, Bayesian process, and posterior computation, the e-book succinctly describes EM-type algorithms, Monte Carlo simulation, numerical suggestions, and optimization equipment. It then provides special posterior suggestions for difficulties, reminiscent of nonresponses in surveys and cross-over trials with lacking values. It additionally presents noniterative posterior sampling ideas for difficulties, equivalent 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 versions. The textual content concludes with a dialogue on compatibility, a primary factor in Bayesian inference.
This e-book deals a unified remedy of an array of statistical difficulties that contain lacking information and limited parameters. It exhibits how Bayesian approaches will be priceless 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