NDLI: Inference in hybrid Bayesian networks using dynamic discretization

Content Provider	Springer Nature Link
Author	Neil, Martin Tailor, Manesh Marquez, David
Copyright Year	2007
Abstract	We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.
Starting Page	219
Ending Page	233
Page Count	15
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	17
Issue Number	3
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2007-07-14
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian networks Expert systems Bayesian software Reasoning under uncertainty Statistical inference Propagation algorithms Dynamic discretization Artificial Intelligence (incl. Robotics) Mathematical Modeling and Industrial Mathematics Numeric Computing Statistics Statistics and Computing/Statistics Programs
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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