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  • 标题:Bayesian Parametric Bootstrap for Models with Intractable Likelihoods
  • 本地全文:下载
  • 作者:Brenda N. Vo ; Christopher C. Drovandi ; Anthony N. Pettitt
  • 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:1
  • 页码:211-234
  • DOI:10.1214/17-BA1071
  • 语种:English
  • 出版社:International Society for Bayesian Analysis
  • 摘要:In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods. The approach is most appealing when the computationally efficient semi-automatic approximate Bayesian computation (ABC) summary statistics are selected. The parametric bootstrap approximation is used to form a proposal distribution in ABC algorithms to improve the computational efficiency. The new approach is demonstrated through the sequential Monte Carlo and the ABC importance and rejection sampling algorithms. We found efficiency gains in two simulation studies, the univariate g-and-k quantile distribution, a toggle switch model in dynamic bionetworks, and in a stochastic model describing expanding melanoma cell colonies.
  • 关键词:Bayesian parametric bootstrap; approximate Bayesian computation;sequential Monte Carlo; melanoma cell spreading; agent-based model; quantile distribution.
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