87 citations · 166 across the 13 of their papers we have counts for
7 papers · 1 filter
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Changhe Yuan, Marek J. Druzdzel
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Ev…
An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Changhe Yuan, Brandon Malone
Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that o…
Annealed MAP
Changhe Yuan, Tsai-Ching Lu, Marek J. Druzdzel
Maximum a Posteriori assignment (MAP) is the problem of finding the most probable instantiation of a set of variables given the partial evidence on the other variables in a Bayesia…
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…