Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH scholarly article en Jaeger, Manfred License
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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 =	{Jaeger, Manfred},
  title =	{{Importance Sampling on Relational Bayesian Networks}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--16},
  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 =		{},
  URN =		{urn:nbn:de:0030-drops-4116},
  doi =		{10.4230/DagSemProc.05051.7},
  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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