172 citations · 537 across the 21 of their papers we have counts for
21 papers
An Algorithm for Computing Probabilistic Propositions
Gregory F. Cooper
A method for computing probabilistic propositions is presented. It assumes the availability of a single external routine for computing the probability of one instantiated variable,…
Stochastic Simulation of Bayesian Belief Networks
Homer L. Chin, Gregory F. Cooper
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…
Updating Probabilities in Multiply-Connected Belief Networks
Jaap Suermondt, Gregory F. Cooper
This paper focuses on probability updates in multiply-connected belief networks. Pearl has designed the method of conditioning, which enables us to apply his algorithm for belief u…
A Method for Using Belief Networks as Influence Diagrams
Gregory F. Cooper
This paper demonstrates a method for using belief-network algorithms to solve influence diagram problems. In particular, both exact and approximation belief-network algorithms may…
KNET: Integrating Hypermedia and Bayesian Modeling
R. Martin Chavez, Gregory F. Cooper
KNET is a general-purpose shell for constructing expert systems based on belief networks and decision networks. Such networks serve as graphical representations for decision models…
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
Eric J. Horvitz, Jaap Suermondt, Gregory F. Cooper
We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belie…