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Importance sampling in Bayesian networks using probability trees

✍ Scribed by Antonio Salmerón; Andrés Cano; Serafı́n Moral

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
248 KB
Volume
34
Category
Article
ISSN
0167-9473

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✦ Synopsis

In this paper a new Monte-Carlo algorithm for the propagation of probabilities in Bayesian networks is proposed. This algorithm has two stages: in the ÿrst one an approximate propagation is carried out by means of a deletion sequence of the variables. In the second stage a sample is obtained using as sampling distribution the calculations of the ÿrst step. The di erent conÿgurations of the sample are weighted according to the importance sampling technique. We show how the use of probability trees to store and to approximate probability potentials, and a careful selection of the deletion sequence, make this algorithm able to propagate over large networks with extreme probabilities.

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