Stochastic simulation of causal Bayesian models.
Chin HL, Cooper GF. Stochastic simulation of causal Bayesian models. In: Uncertainty in Artificial Intelligence 3 (North-Holland, Amsterdam, 1989) 129–147.
This paper examines the use of stochastic simulation of Bayesian belief networks as a method for computing the probabilities of values of variables. Specifically, it examines the use of a scheme described by Henrion, called logic sampling, and an extension to that scheme described by Pearl. The scheme devised by Pearl allows us to "clamp" any number of variables to given values and to conduct stochastic simulation on the resulting network. We have found that this algorithm, in certain networks, leads to much slower than expected convergence to the true posterior probability. This behavior is a result of the tendency for local areas in the graph to become fixed through many stochastic iterations. The length of this non-convergence can be made arbitrarily long by strengthening the dependency between two nodes. This paper describes the use of graph modification. By modifying a belief network through the use of pruning, arc reversal, and node reduction, it may be possible to convert the network to a form that is computationally more efficient for stochastic simulation.
|Chin Cooper.pdf||340.16 KB|