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Structure learning is a key problem in using Bayesian networks for data mining tasks but its computation complexity increases dramatically with the number of features in the dataset. Thus, it is computationally intractable to extend structure learning to large networks without using a scalable parallel approach. This work explores computation primitives to parallelize the first phase of Cheng et al...
Evidence propagation is a major step in exact inference, a key problem in exploring probabilistic graphical models. In this paper, we propose a novel approach for parallelizing evidence propagation in junction trees on clusters. Our proposed method explores structural parallelism in a given junction tree. We decompose a junction tree into a set of subtrees, each consisting of one or multiple leaf-root...
We investigate data parallelism for belief propagation in a cyclic factor graphs on multicore/many core processors. Belief propagation is a key problem in exploring factor graphs, a probabilistic graphical model that has found applications in many domains. In this paper, we identify basic operations called node level primitives for updating the distribution tables in a factor graph. We develop algorithms...
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