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  • 标题:Semiparametric Regression Analysis via Infer.NET
  • 本地全文:下载
  • 作者:Jan Luts ; Shen S. J. Wang ; John T. Ormerod
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2018
  • 卷号:87
  • 期号:1
  • 页码:1-37
  • DOI:10.18637/jss.v087.i02
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要:We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models. The examples are chosen to encompass a wide range of semiparametric regression situations. Infer.NET is shown to produce accurate inference in comparison with Markov chain Monte Carlo via the BUGS package, but to be considerably faster. Potentially, this contribution represents the start of a new era for semiparametric regression, where large and complex analyses are performed via fast Bayesian inference methodology and software, mainly being developed within Machine Learning.
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