Parallel Computing for Bayesian Networks

Most problems in Bayesian network theory have a computational complexity that, in the worst case, scales exponentially with the number of variables. It is polynomial even for sparse networks. Even though newer algorithms are designed to improve scalability, it is unfeasible to analyze data containing more than a few hundreds of variables. Parallel computing provides a way to address this problem by making better use of modern hardware.

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