Robert M. Fung

UAI Conference 1994 Conference Paper

Backward Simulation in Bayesian Networks

• Robert M. Fung
• Brendan Del Favero

UAI Conference 1991 Conference Paper

Symbolic Probabilistic Inference with Continuous Variables

• Kuo-Chu Chang
• Robert M. Fung

UAI Conference 1991 Conference Paper

Symbolic Probabilistic Inference with Evidence Potential

• Kuo-Chu Chang
• Robert M. Fung

AAAI Conference 1990 Conference Paper

Constructor: A System for the Induction of Probabilistic Models

• Robert M. Fung

The probabilistic network technology is a knowledgebased technique which focuses on reasoning under uncertainty. Because of its well defined semantics and solid theoretical foundations, the technology is finding increasing application in fields such as medical diagnosis, machine vision, military situation assessment, petroleum exploration, and information retrieval. However, like other knowledge-based techniques, acquiring the qualitative and quantitative information needed to build these networks can be highly labor-intensive. CONSTRUCTQR integrates techniques and concepts from probabilistic networks, artificial intelligence, and statistics in order to induce Markov networks (i. e. , undirected probabilistic networks). The resulting networks are useful both qualitatively for concept organization and quantitatively for the assessment of new data. The primary goal of CONSTRUCTOR is to find qualitative structure from data. CONSTRUCTOR finds structure by first, modeling each feature in a data set as a node in a Markov network and secondly, by finding the neighbors of each node in the network. In Markov networks, the neighbors of a node have the property of being the smallest set of nodes which “shield” the node from being affected by other nodes in the graph. This property is used in a heuristic search to identify each node’ s neighbors. The traditional x2 test for independence is used to test if a set of nodes “shield” another node. Cross-validation is used to estimate the quality of alternative structures.

UAI Conference 1990 Conference Paper

Refinement and coarsening of Bayesian networks

• Kuo-Chu Chang
• Robert M. Fung

UAI Conference 1989 Conference Paper

Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks

• Robert M. 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 set of simulation trials. This paper describes the evidence weighting mechanism, for augmenting the logic sampling stochastic simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic sampling algorithm by weighting each simulation trial by the likelihood of a network's evidence given the sampled state node values for that trial. We also describe an enhancement to the basic algorithm which uses the evidential integration technique [Chin and Cooper, 1987]. A comparison of the basic evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987], the logic sampling algorithm, and the evidence integration algorithm is presented. The comparison is aided by analyzing the performance of the algorithms in a simple example network.

UAI Conference 1985 Conference Paper

Metaprobability and Dempster-Shafer in Evidential Reasoning

• Robert M. Fung
• Chee Yee Chong