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MapReduce guided approximate inference over graphical models
2014
2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
A graphical model represents the data distribution of a data generating process and inherently captures its feature relationships. This stochastic model can be used to perform inference, to calculate posterior probabilities, in various applications such as classification. Exact inference algorithms are known to be intractable on large networks due to exponential time and space complexity. Approximate inference algorithms are instead widely used in practice to overcome this constraint, with a
doi:10.1109/cidm.2014.7008702
dblp:conf/cidm/HaqueCKB14
fatcat:el5g5p4duvfvbjmbyysq34ylqm