William Lam

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We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a continuous-time Markov process. This software provides libraries and programs for most of the algorithms developed for CTBNs. For learning, CTBN-RLE implements structure and(More)
We consider filtering for a continuous-time, or asynchronous, stochastic system where the full distribution over states is too large to be stored or calculated. We assume that the rate matrix of the system can be compactly represented and that the belief distribution is to be approximated as a product of marginals. The essential computation is the matrix(More)
We report the PASCAL2 benchmark for DAOOPT and GUROBI on MPE task with 330 optimally solved instances from 8 benchmark domains. DAOOPT out-performed GUROBI in 3 domains, while GUROBI was faster than DAOOPT in the rest of the 5 domains. We show that DAOOPT performed well in domains where it could have high quality initial solutions for pruning the AND/OR(More)
Clinical trials and independent reviews support the use of cholinesterase inhibitors for treating the symptoms of patients with mild to moderate Alzheimer's disease (AD). Before initiating cholinesterase inhibitor therapy, patients should be thoroughly assessed, and the diagnosis confirmed, preferably by a specialist. Compliance with cholinesterase(More)
The paper investigates the potential of look-ahead in the context of AND/OR search in graphical models using the Mini-Bucket heuristic for combinatorial optimization tasks (e.g., MAP/MPE or weighted CSPs). We present and analyze the complexity of computing the residual (a.k.a. Bellman update) of the Mini-Bucket heuristic and show how this can be used to(More)
The paper explores the potential of look-ahead methods within the context of AND/OR search in graphical models using the Mini-Bucket heuristic for combinato-rial optimization tasks (e.g., weighted CSPS or MAP inference). We study how these methods can be used to compensate for the approximation error of the initially generated Mini-Bucket heuristics, within(More)
We explore the use of iterative cost-shifting as a dynamic heuristic generator for solving MPE in graphi-cal models via Branch and Bound. When mini-bucket elimination is limited by its memory budget, it may not provide good heuristics. This can happen often when the graphical model has a very high induced width with large variable domain sizes. In addition,(More)
Acknowledgments I am deeply grateful to my adviser, Dr. Christian R. Shelton, for his invaluable mentoring. Without his help I would not have been able to complete this dissertation. I thank my committee members: Dr. Lonardi for his technical advise, Dr. Hanne-man for his time and advice with social network analysis, and Dr. Keogh for his generous support(More)