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  • 标题:Analysis of Polya-Gamma Gibbs sampler for Bayesian logistic analysis of variance
  • 本地全文:下载
  • 作者:Hee Min Choi ; Jorge Carlos Román
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2017
  • 卷号:11
  • 期号:1
  • 页码:326-337
  • DOI:10.1214/17-EJS1227
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 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.
  • 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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