Representation and Reasoning for Recursive Probability Models
Date
2006
Authors
Howard, C.M.
Stumptner, M.
Editors
Sattar, A.
Kang, B.H.
Kang, B.H.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006 / Sattar, A., Kang, B.H. (ed./s), vol.4304 LNAI, pp.120-130
Statement of Responsibility
Conference Name
19th Australian Joint Conference on Artificial Intelligence (4 Dec 2006 - 8 Dec 2006 : Hobart, Australia)
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. © Springer-Verlag Berlin Heidelberg 2006.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright status unknown