DagSemProc.05051.7.pdf
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Importance Sampling on Relational Bayesian Networks
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Dagstuhl Seminar Proceedings, Volume 5051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-01-19
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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
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.
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- Relational models
- Importance Sampling
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