Analysis of Polya-Gamma Gibbs sampler for Bayesian logistic analysis of variance

ELECTRONIC JOURNAL OF STATISTICS（2017）

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摘要

We consider the intractable posterior density that results when the one-way logistic analysis of variance model is combined with a flat prior. We analyze Polson, Scott and Windle's (2013) data augmentation (DA) algorithm for exploring the posterior. The Markov operator associated with the DA algorithm is shown to be trace-class.

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关键词

Polya-Gamma distribution,data augmentation algorithm,geometric convergence rate,Markov chain,Markov operator,Monte Carlo,trace-class operator

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