Premium
Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals
Author(s) -
Shenoy Prakash P.,
Rumí Rafael,
Salmerón Antonio
Publication year - 2015
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21700
Subject(s) - computer science , bayesian network , key (lock) , inference , hypercube , task (project management) , theoretical computer science , artificial intelligence , algorithm , computer security , management , parallel computing , economics
In this paper, we discuss some practical issues that arise in solving 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 difficulty in handling deterministic conditionals (for continuous variables). We propose some strategies for carrying out the inference task using mixtures of polynomials (MOPs) and mixtures of truncated exponentials. MOPs can be defined on hypercubes or hyperrhombuses. We compare these two methods. A key strategy is to reapproximate large potentials with potentials consisting of fewer pieces and lower degrees/number of terms. We discuss several methods for reapproximating potentials. We illustrate our methods in a practical application consisting of solving a stochastic program evaluation and review technique (PERT) network.