Learning Structures of Bayesian Networks for Variable Groups

31 Aug 2015 Pekka Parviainen Samuel Kaski

Bayesian networks, and especially their structures, are powerful tools for representing conditional independencies and dependencies between random variables. In applications where related variables form a priori known groups, chosen to represent different "views" to or aspects of the same entities, one may be more interested in modeling dependencies between groups of variables rather than between individual variables... (read more)

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