Unsupervised Learning of Noisy-Or Bayesian Networks

Understand

This paper considers the problem of learning the parameters in Bayesian networks of discrete variables with known structure and hidden variables.

• Previous approaches in these settings typically use expectation maximization; when the network has high treewidth, the required expectations might be approximated using Monte Carlo or variational methods.
• We show how to avoid inference altogether during learning by giving a polynomial-time algorithm based on the method-of-moments, building upon recent work on learning discrete-valued mixture models.
• In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks.