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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 adoi:10.1109/cidm.2014.7008702 dblp:conf/cidm/HaqueCKB14 fatcat:el5g5p4duvfvbjmbyysq34ylqm