Stochastic Belief Propagation on Trees
نویسنده
چکیده
For exact inference, Belief Propagation(BP) on trees requires O(Td2) operations, where T is the number of variables and d is the cardinality of all hidden variables. This quadratic complexity becomes prohibitive when d is large. Stochastic Belief Propagation(SBP)[1] is an approximate inference algorithm which utilizes subtle changes to original BP in order to achieve O(1/ √ τ) error in O(τTd) time over all trees. The SBP algorithm is described herein and illustrated for HMMs, and interesting avenues for further work on SBP are discussed.
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