Methods for learning Bayesian networks can discover dependency structure between observed variables. Al though these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper, we consider a solution that is applicable when many variables have similar behavior. We intro duce a new class of models, module networks, that ex plicitly partition the variables into modules that share the same parents in the network and the same condi tional probability distribution. We define the seman tics of module networks, and describe an algorithm that learns the modules' composition and their depen dency structure from data. Evaluation on real data in the domains of gene expression and the stock mar ket shows that module networks generalize better than Bayesian networks, and that the learned module net work structure reveals regularities that are obscured in learned Bayesian networks.
Segal et al. REF introduce module networks which construct groups of variables with "similar behaviour" (i.e., variables that share the same set of parents and the same parameters), called modules.
Learning Module Networks
ArXiv
ArXiv
Mathematics Computer Science