The method of conditioning permits probabilistic inference in multiply connected belief networks using an algorithm by Pearl. This method uses a select set of nodes, the loop cutset, to render the multiply connected network singly connected. We discuss the function of the nodes of the loop cutset and a condition that must be met by the nodes of the loop… (More)
Each of the variables in a large probabilistic model may be relevant for some types of reasoning within this model, but rarely will all of them participate in reasoning related to a single query. We review a variety of schemes to identify variables that given certain observations are relevant to a query of interest .
We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange… (More)
Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use.… (More)
Suermondt, H. The method of conditioning allows us to use Pearl's probabilistic-inference algorithm in multiply connected belief networks by instantiating a subset of the nodes in the network, the loop cutset. To use the method of conditioning, we must calculate the joint prior probabilities of the nodes of the loop cutset. We present a method that lets us… (More)
Cutset conditioning and clique-tree propagation are two popular methods for exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We characterize network structures in which the performances of these methods… (More)
Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We describe a means to combine cutset conditioning and clique- tree… (More)
We demonstrate a fully automated method for obtaining a closed-form approximation of a recursive function. This method resulted from a real-world problem in which we had a detector that monitors a time series and where we needed an indication of the total number of false positives expected over a fixed amount of time. The problem, because of the constraints… (More)
(1989). The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks.