Discovering new intermediate variables in Bayesian networks
Bayesian networks have been used to model various types of cognitive process- ing, but have so far lacked structure learning capabilities, in particular the capa- bility to discover and integrate new variables. The paper proposes a method that automatically adds new intermediate variables that compress information coming into nodes, based on the properties of probability distributions in the network. This serves as a proof-of-concept method of adding new variables to Bayesian net- works. To add nodes in an informed manner, the method utilizes metrics from information theory such as entropy and Kullback-Leibler divergence.
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