Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals

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Issue Date
2015-03Author
Shenoy, Prakash P.
Rumi, Rafael
Salmeron, Antonio
Publisher
Wiley
Type
Article
Article Version
Scholarly/refereed, author accepted manuscript
Metadata
Show full item recordAbstract
In this paper we discuss some practical issues that arise in solv-
ing hybrid Bayesian networks that include deterministic conditionals
for continuous variables. We show how exact inference can become
intractable even for small networks, due to the di culty in handling
deterministic conditionals (for continuous variables). We propose some
strategies for carrying out the inference task using mixtures of polyno-
mials and mixtures of truncated exponentials. Mixtures of polynomials
can be de ned on hypercubes or hyper-rhombuses. We compare these
two methods. A key strategy is to re-approximate large potentials
with potentials consisting of fewer pieces and lower degrees/number
of terms. We discuss several methods for re-approximating potentials.
We illustrate our methods in a practical application consisting of solv-
ing a stochastic PERT network.
Description
This is the author's final draft. Copyright 2015 Wiley
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Citation
Shenoy, Prakash P., Rafael Rumí, and Antonio Salmerón. "Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals." International Journal of Intelligent Systems Int. J. Intell. Syst. 30.3 (2014): 265-91. doi: http://dx.doi.org/10.1002/int.21700.
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