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Learning hybrid Bayesian networks from data
, 1998
"... We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first method ..."
Abstract

Cited by 11 (1 self)
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We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original nondiscretized data for the parameterization of the learned structure. The second methodology uses nondiscretized data both to learn the Bayesian network structure and its parameterization. For the direct handling of continuous data, we propose the use of artificial neural networks as probability estimators, to be used as an integral part of the scoring metric defined to search the space of Bayesian network structures. With both methodologies, we assume the availability of a complete dataset, with no missing values or hidden variables. We report experimental results aimed at comparing the two methodologies. These results provide evidence that learning with discretized data presents advantages both in terms of efficiency and in terms of accuracy of the learned models over the alternative approach of using nondiscretized data.
Bounded RD
"... This paper presents a new inference algorithm for belief networks that combines a searchbased algorithm with a simulationbased algorithm. The former is an extension of the recursive decomposition (RD) algorithm proposed by Cooper in [8], which is here modi ed to compute interval bounds on margina ..."
Abstract
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This paper presents a new inference algorithm for belief networks that combines a searchbased algorithm with a simulationbased algorithm. The former is an extension of the recursive decomposition (RD) algorithm proposed by Cooper in [8], which is here modi ed to compute interval bounds on marginal probabilities. We call the algorithm boundedRD. The latter is a stochastic simulation method known as Pearl's Markov blanket algorithm [31]. Markov simulation is used to generate highly probable instantiations of the network nodes to be used by boundedRD in the computation of probability bounds. BoundedRD has the anytime property, and produces successively narrower interval bounds, which converge in the limit to the exact value. 1