
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We consider the NPhard problem of MAPinference for undirected discrete...
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Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Arising from many applications at the intersection of decision making an...
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Approximate MMAP by Marginal Search
We present a heuristic strategy for marginal MAP (MMAP) queries in graph...
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Partial Optimality by Pruning for MAPInference with General Graphical Models
We consider the energy minimization problem for undirected graphical mod...
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Annealed MAP
Maximum a Posteriori assignment (MAP) is the problem of finding the most...
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Inference with Seperately Specified Sets of Probabilities in Credal Networks
We present new algorithms for inference in credal networks  directed ...
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Bethe Bounds and Approximating the Global Optimum
Inference in general Markov random fields (MRFs) is NPhard, though iden...
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Maximum A Posteriori Inference in SumProduct Networks
Sumproduct networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NPhard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NPhard to approximate the MAP problem to 2^n^ϵ for fixed 0 ≤ϵ < 1, where n is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on realworld datasets show that it has better overall performance than existing approximate solvers.
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