License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05051.6
URN: urn:nbn:de:0030-drops-4157
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/415/
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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: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/opus/volltexte/2006/415},
  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}
}

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

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