Abstract de nuestro grupo en 2000/UTAI Research Group Abstracts in 2000


Importance sampling in Bayesian networks using probability trees
BY A. Salmerón, A. Cano, S. Moral

Computational Statistics and Data Analysis (Accepted), 2000.

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 first 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 first step. The different configurations of the sample are weighted according to the importance sampling technique. One important characteristic of the procedure is the use of probability trees to store and approximate probability potentials, showing a significative difference with respect to the case in which potentials are represented by means of tables.


A new approach for learning belief networks using independence criteria
BY Luis M. de Campos, Juan F. Huete

International Journal of Approximate Reasoning 24 n.1, 11-37, 2000.

In the paper we describe a new independence-based approach for learning Belief Networks. The proposed algorithm avoids some of the drawbacks of this approach by making an intensive use of low order conditional independence tests. Particularly, the set of zero and first order independence statements are used in order to obtain a prior skeleton of the network, and also to fix and remove arrows from this skeleton. Then, a refinement procedure, based on minimum cardinality d-separating sets, which uses a small number of conditional independence tests of higher order, is carried out to produce the final graph. Our algorithm needs an ordering of the variables in the model as the input. An algorithm that partially overcomes this problem is also presented.


Penniless Propagation in Join Trees
BY A. Cano, S. Moral, A. Salmerón

International Journal of Intelligent Systems.Volume 15, Issue 11, pages 1027-1059, 2000.

This paper presents non-random algorithms for approximate computation in Bayesian networks. They are based on the use of probability trees to represent probability potentials, using the Kullback-Leibler cross entropy as a measure of the error of the approximation. Different alternatives are presented and tested in several experiments with difficult propagation problems. The results show how it is possible to find good approximations in short time compared with Hugin algorithm.



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