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Documents authored by Cussens, James


Document
Model equivalence of PRISM programs

Authors: James Cussens

Published in: Dagstuhl Seminar Proceedings, Volume 7161, Probabilistic, Logical and Relational Learning - A Further Synthesis (2008)


Abstract
The problem of deciding the probability model equivalence of two PRISM programs is addressed. In the finite case this problem can be solved (albeit slowly) using techniques from emph{algebraic statistics}, specifically the computation of elimination ideals and Gr"{o}bner bases. A very brief introduction to algebraic statistics is given. Consideration is given to cases where shortcuts to proving/disproving model equivalence are available.

Cite as

James Cussens. Model equivalence of PRISM programs. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2008)


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@InProceedings{cussens:DagSemProc.07161.7,
  author =	{Cussens, James},
  title =	{{Model equivalence of PRISM programs}},
  booktitle =	{Probabilistic, Logical and Relational Learning - A Further Synthesis},
  pages =	{1--21},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7161},
  editor =	{Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting 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.07161.7},
  URN =		{urn:nbn:de:0030-drops-13808},
  doi =		{10.4230/DagSemProc.07161.7},
  annote =	{Keywords: PRISM programs, model equivalence, model inclusion, algebraic statistics, algebraic geometry, ideals, varieties, Gr"\{o\}bner bases, polynomials}
}
Document
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.

Cite as

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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