Semiparametric Regression Analysis via Infer.NET | Zendy

Jan Luts | Zendy; Shen S. J. Wang | Zendy; John T. Ormerod | Zendy; M. P. Wand | Zendy

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Semiparametric Regression Analysis via Infer.NET

Author(s) -

Jan Luts,

Shen S. J. Wang,

John T. Ormerod,

M. P. Wand

Publication year - 2018

Publication title -

journal of statistical software

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.636

H-Index - 145

ISSN - 1548-7660

DOI - 10.18637/jss.v087.i02

Subject(s) - markov chain monte carlo , computer science , inference , bayesian probability , semiparametric regression , bayesian inference , bayesian linear regression , range (aeronautics) , machine learning , regression , r package , artificial intelligence , econometrics , regression analysis , statistics , mathematics , engineering , aerospace engineering , computational science

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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