Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Jan Luts, Shen S. J. Wang, John T. Ormerod, Matt P. Wand
Title: Semiparametric Regression Analysis via Infer.NET
Abstract: 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.

Page views:: 2504. Submitted: 2014-02-12. Published: 2018-10-31.
Paper: Semiparametric Regression Analysis via Infer.NET     Download PDF (Downloads: 1130)
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DOI: 10.18637/jss.v087.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.