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.05051.7
URN: urn:nbn:de:0030-drops-4116
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/411/
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Jaeger, Manfred

Importance Sampling on Relational Bayesian Networks

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05051.JaegerManfred.Paper.411.pdf (0.2 MB)

Abstract

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

@InProceedings{jaeger:DagSemProc.05051.7,
  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 =		{https://drops.dagstuhl.de/opus/volltexte/2006/411},
  URN =		{urn:nbn:de:0030-drops-4116},
  doi =		{10.4230/DagSemProc.05051.7},
  annote =	{Keywords: Relational models, Importance Sampling}
}

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

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