A Monte-Carlo Algorithm for Probabilistic Propagation in Belief Networks based on Importance Sampling and Stratified Simulation Techniques
A class of Monte Carlo algorithms for probability propagation in belief networks is given. The simulation is based on a two steps procedure. The first one is a node deletion technique to calculate the ’a posteriori’ distribution on a variable, with the particularity that when exact computations a...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/4899 https://doi.org/10.1016/S0888-613X(97)10004-4 |