Importance Sampling on Relational Bayesian Networks

Jaeger, Manfred

doi:10.4230/DagSemProc.05051.7

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Importance Sampling on Relational Bayesian Networks

Author Manfred Jaeger

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 5051
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2006-01-19

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DagSemProc.05051.7.pdf

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Document Identifiers

DOI: 10.4230/DagSemProc.05051.7
URN: urn:nbn:de:0030-drops-4116

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Keywords

Relational models
Importance Sampling

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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.

Cite As Get BibTex

Manfred Jaeger. Importance Sampling on Relational Bayesian Networks. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006) https://doi.org/10.4230/DagSemProc.05051.7

Author Details

Manfred Jaeger

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