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Exploiting independence for branch operations in Bayesian learning of C&RTs

Authors: Nicos Angelopoulos and James Cussens

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract

In this paper we extend a methodology for Bayesian learning via MCMC, with the ability to grow arbitrarily long branches in C&RT models. We are able to do so by exploiting independence in the model construction process. The ability to grow branches rather than single nodes has been noted as desirable in the literature. The most singular feature of the underline methodology used here in comparison to other approaches is the coupling of the prior and the proposal. The main contribution of this paper is to show how taking advantage of independence in the coupled process, can allow branch growing and swapping for proposal models.

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Nicos Angelopoulos and James Cussens. Exploiting independence for branch operations in Bayesian learning of C&RTs. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{angelopoulos_et_al:DagSemProc.05051.6,
  author =	{Angelopoulos, Nicos and Cussens, James},
  title =	{{Exploiting independence for branch operations in Bayesian learning of C\&RTs}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.6},
  URN =		{urn:nbn:de:0030-drops-4157},
  doi =		{10.4230/DagSemProc.05051.6},
  annote =	{Keywords: Bayesian machine learning, classification and regression trees, stochastic logic programs}
}

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Exploiting independence for branch operations in Bayesian learning of C&RTs

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