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URN: urn:nbn:de:0030-drops-4116

Importance Sampling on Relational Bayesian Networks



We present techniques for importance sampling from distributions defined by Relational Bayesian Networks. The methods operate directly on the abstract representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Furthermore, we use in our experiments a model that illustrates a fully general way of translating the recent framework of Markov Logic Networks into Relational Bayesian Networks.

BibTeX - Entry

  author =	{Manfred Jaeger},
  title =	{Importance Sampling on Relational Bayesian Networks},
  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 =		{},
  annote =	{Keywords: Relational models, Importance Sampling}

Keywords: Relational models, Importance Sampling
Seminar: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue date: 2006
Date of publication: 19.01.2006

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