KSL-87-22
## Stochastic Simulation of Casual Bayesian Models

**Reference: **
Chin, H. L. &
Cooper, G. F. Stochastic Simulation of Casual Bayesian Models. North-Holland, 1988.

**Abstract:** This paper examines Bayesian belief network inference using simulation as a
method for computing the posterior probabilities of network variables.
Specifically, it examines the use of a method described by Henrion, called
logic sampling, and a method described by Pearl, called stochastic simulation.
We first review the conditions under which logic sampling is computationally
infeasible. Such cases motivated the development of the Pearl's stochastic
simulation algorithm. We have found that this stochastic simulation
algorithm, when applied to certain networks, leads to much slower than
expected convergence to the true posterior probabilities. This behavior is a
result of the tendency for local areas in the network to become fixed through
many simulation cycles. The time required to obtain significant convergence
can be made arbitrarily long be strengthening the proabilistic dependency
between nodes. We propose the use of several forms of graph modification,
such as graph pruning, arc reversal, and node reduction, in order to convert
some networks into formats that are computationally more efficient for
simulation.

**Notes:** Chapter: Bayesian Belief Network Inference Using Simulation.

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