Dear colleagues,
The following reference may be of your interest:
Antonio Salmeron, Andres Cano, Serafin Moral (2000)
"Importance sampling in Bayesian networks using probability trees".
Computational Statistics and Data Analysis, Vol. 34, pages 387-413.
Abstract: 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. 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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