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URN: urn:nbn:de:0030-drops-4157
URL: http://drops.dagstuhl.de/opus/volltexte/2006/415/
Angelopoulos, Nicos ; Cussens, James

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

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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.

BibTeX - Entry

@InProceedings{angelopoulos_et_al:DSP:2006:415,
  author =	{Nicos Angelopoulos and James Cussens},
  title =	{Exploiting independence for branch operations in Bayesian learning of C&RTs},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  year =	{2006},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor  and Stephen H. Muggleton},
  number =	{05051},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2006/415},
  annote =	{Keywords: Bayesian machine learning, classification and regression trees, stochastic logic programs}
}

Keywords: Bayesian machine learning, classification and regression trees, stochastic logic programs
Seminar: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue date: 2006
Date of publication: 08.02.2006

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