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Developing efficient (i.e. polynomial time) algorithms with guaranteed performance is a central goal in computer science (perhaps *the* central goal). In machine learning, inference algorithms meeting these requirements are much rarer than we would like: often, an algorithm is either efficient but doesn’t perform optimally or vice versa. A number of results from the 1990’s demonstrate the challenges of, but also the potential for, efficient Bayesian inference. These results were carried out in the context of Bayesian networks.

Continue reading “Complexity of Inference in Bayesian Networks”