Probababilistic Decision Graphs - Combining Verification and AI Techniques for 
Probabilistic Inference

Jaeger, Manfred

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Probababilistic Decision Graphs - Combining Verification and AI Techniques for Probabilistic Inference

Jaeger, M. (2002). Probababilistic Decision Graphs - Combining Verification and AI Techniques for Probabilistic Inference. In Proceedings of the First European Workshop on Probabilistic Graphical Models (pp. 81-88). -: Computer Science Department, University of Castilla - La Mancha.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-3036-9 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-3038-5

Genre: Conference Paper

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Creators:
Jaeger, Manfred¹, Author

Affiliations:
1Programming Logics, MPI for Informatics, Max Planck Society, ou_40045

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Abstract: We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic decision graph for a given distribution is at most as large as the smallest junction tree for the same distribution, and that in some cases it can in fact be much smaller. Behind these very promising features of probabilistic decision graphs lies the fact that they integrate into a single coherent framework a number of representational and algorithmic optimizations developed for Bayesian networks (use of hidden variables, context-specific independence, structured representation of conditional probability tables).

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Language(s): eng - English

Dates: Modified: 2003-08-12Date issued: 2002

Publication Status: Issued

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Table of Contents: -

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Identifiers: eDoc: 202107
Other: Local-ID: C1256104005ECAFC-8B96E7D1BCDFF6ECC1256CAF00678172-JaegerPGM02

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Title: European Workshop on Probabilistic Graphical Models

Place of Event: Cuenca, Spain

Start-/End Date: 2002-11-06

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Title: Proceedings of the First European Workshop on Probabilistic Graphical Models

Source Genre: Proceedings

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Publ. Info: - : Computer Science Department, University of Castilla - La Mancha

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 81 - 88 Identifier: -