14 citations · 33 across the 4 of their papers we have counts for
4 papers
Importance Sampling in Bayesian Networks: An Influence-Based Approximation Strategy for Importance Functions
Changhe Yuan, Marek J. Druzdzel
One of the main problems of importance sampling in Bayesian networks is representation of the importance function, which should ideally be as close as possible to the posterior joi…
Most Relevant Explanation: Properties, Algorithms, and Evaluations
Changhe Yuan, Xiaolu Liu, Tsai-Ching Lu +1
Most Relevant Explanation (MRE) is a method for finding multivariate explanations for given evidence in Bayesian networks [12]. This paper studies the theoretical properties of MRE…
Solving Multistage Influence Diagrams using Branch-and-Bound Search
Changhe Yuan, Xiaojian Wu, Eric A. Hansen
A branch-and-bound approach to solving influ- ence diagrams has been previously proposed in the literature, but appears to have never been implemented and evaluated - apparently du…
Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search
Brandon Malone, Changhe Yuan, Eric A. Hansen +1
Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A*…