Representation and reasoning for recursive probability models

Authors:
Catherine Howard;Markus Stumptner
Affiliations:
Electronic Warfare and Radar Division, Defence Science and Technology Organisation, Edinburgh, South Australia;Advanced Computing Research Center, University of South Australia, Adelaide
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 2
Cited 0

Semantics and Inference for Recursive Probability Models

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Probabilistic reasoning for complex systems

Probabilistic reasoning for complex systems

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Abstract

This paper applies the Object Oriented Probabilistic Relational Modelling Language to recursive probability models. We present two novel anytime inference algorithms for recursive probability models expressed using this language. We discuss the strengths and limitations of these algorithms and compare their performance against the Iterative Structured Variable Elimination algorithm proposed for Probabilistic Relational Modelling Language using three different non-linear genetic recursive probability models.