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Belief networks have become an increasingly popular mechanism for dealing with uncertainty in systems. Unfortunately, it is known that finding the probability values of belief network nodes given a set of evidence is not tractable in general. Many different simulation algorithms for approximating solutions to this problem have been proposed and implemented. In this report, we describe the implementation of a collection of such algorithms, CABeN. CABeN contains a library of routines for simulating belief networks, a program for accessing the routines through menus on any `tty' interface, and some sample programs demonstrating how the library would be used within an application. CABeN implements five algorithms: Logic Sampling, Likelihood Weighting (Shachter's Basic algorithm), Self Importance, Pearl's algorithm, and Chavez's algorithm. In addition, we have implemented Markov scoring as an option to any of the above algorithms. We have compared these 10 variations with each other in a se..

Second session of Conference in the Matter of Pollution of the Interstate Waters of Escambia River Basin (Alabama-Florida) and the Intrastate Portions of the Escambia Basin and Bay within the State of Florida; transcript of proceedings.

Supt. of Docs. no.: EP2.2.:Es1/971Mode of access: Internet