most citedRefinement and Coarsening of Bayesian Networks

37 citations · 42 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI20131 cited

Metaprobability and Dempster-Shafer in Evidential Reasoning

Robert Fung, Chee Yee Chong

Evidential reasoning in expert systems has often used ad-hoc uncertainty calculi. Although it is generally accepted that probability theory provides a firm theoretical foundation,…

cs.AI20134 cited

Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks

Robert Fung, Kuo-Chu Chang

Stochastic simulation approaches perform probabilistic inference in Bayesian networks by estimating the probability of an event based on the frequency that the event occurs in a se…

cs.AI201337 cited

Refinement and Coarsening of Bayesian Networks

Kuo-Chu Chang, Robert Fung

In almost all situation assessment problems, it is useful to dynamically contract and expand the states under consideration as assessment proceeds. Contraction is most often used t…

cs.AI2013

Symbolic Probabilistic Inference with Evidence Potential

Kuo-Chu Chang, Robert Fung

Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[2] has focused attention on the importance of resolving general queries in Bayesian networks. SPI applies th…

cs.AI2013

Symbolic Probabilistic Inference with Continuous Variables

Kuo-Chu Chang, Robert Fung

Research on Symbolic Probabilistic Inference (SPI) [2, 3] has provided an algorithm for resolving general queries in Bayesian networks. SPI applies the concept of dependency direct…

cs.AI2013

Backward Simulation in Bayesian Networks

Robert Fung, Brendan del Favero

Backward simulation is an approximate inference technique for Bayesian belief networks. It differs from existing simulation methods in that it starts simulation from the known evid…