Averaging of Decomposable Graphs by Dynamic Programming and Sampling
نویسندگان
چکیده
We present algorithms for Bayesian learning of decomposable models from data. Priors of a certain form admit exact averaging in O(3nn3) time and sampling T graphs from the posterior in O(4n + nT) time. To target a broader class of priors we associate each sample with an importance weight. Empirically, we compare averaging to optimization and demonstrate the accuracy of our importance sampling estimates.
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تاریخ انتشار 2015