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  • 标题:Stochastic Approximations to the Pitman–Yor Process
  • 本地全文:下载
  • 作者:Julyan Arbel ; Pierpaolo De Blasi ; Igor Prünster
  • 期刊名称:Bayesian Analysis
  • 印刷版ISSN:1931-6690
  • 电子版ISSN:1936-0975
  • 出版年度:2019
  • 卷号:14
  • 期号:4
  • 页码:1201-1219
  • DOI:10.1214/18-BA1127
  • 语种:English
  • 出版社:International Society for Bayesian Analysis
  • 摘要:In this paper we consider approximations to the popular Pitman–Yor process obtained by truncating the stick-breaking representation. The truncation is determined by a random stopping rule that achieves an almost sure control on the approximation error in total variation distance. We derive the asymptotic distribution of the random truncation point as the approximation error ? goes to zero in terms of a polynomially tilted positive stable random variable. The practical usefulness and effectiveness of this theoretical result is demonstrated by devising a sampling algorithm to approximate functionals of the ? -version of the Pitman–Yor process.
  • 关键词:stochastic approximation; asymptotic distribution; Bayesian Nonparametrics; Pitman–Yor process; random functionals; random probability measure; stopping rule.
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