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Exact Bayesian structure discovery in Bayesian networks requires exponential time and space. Using dynamic programming (DP), the fastest known sequential algorithm computes the exact posterior probabilities of structural features in O(2(d+1)n2^n) time and space, if the number of nodes (variables) in the Bayesian network is n and the in-degree (the number of parents) per node is bounded by a constant d. Here we present a parallel algorithm capable of computing the exact posterior probabilitiesarXiv:1408.1664v3 fatcat:utsr4ncwdrfr7ncwkv75pezziq