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Abstract: Because of computational problems, multidimensional probability distributions must be approximated by distributions which can be defined by a reasonable number of parameters. As a rule, distributions with a special dependence structure (i.e., complying with a system of conditional independence relations) are considered; graphical Markov models and especially Bayesian networks are often used. This paper proposes application of compositional models for this puropose. In addition to a... (Update)
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BibTeX entry: (Update)
@misc{ jirousek-approximating,
author = "R. Jirousek",
title = "On Approximating Multidimensional Probability Distributions by Compositional
Models",
url = "citeseer.ist.psu.edu/633946.html" }
Citations (may not include all citations):312 Introduction to Bayesian Network (context) - Jensen - 1996
175 Graphical Models (context) - Lauritzen - 1996
23 a least square adjustment of a sampled frequency table when .. (context) - Deming, Stephan - 1940
2 admissible simplification of the dependence structure of a s.. (context) - Perez - 1977
1 Composition of probability measures on finite spaces (context) - Jirousek - 1997
1 Graph Modelling without Graphs (context) - Jirousek - 1998
1 Marginalization in composed probabilistic models (context) - Jirousek - 2000
1 Detection of independence relations from persegrams (context) - Jirousek - 2002
1 Bayesian belief networks -- from construction to inference (context) - Boukaert - 1995
1 Possibility theory I -- III International Journal of General.. (context) - de Cooman - 1997
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