Scoring and Searching over Bayesian Networks with Causal and Associative Priors

Understand

A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data.

• In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths in the true network.
• Such beliefs correspond to knowledge about the possible causal and associative relations between pairs of variables.
• This type of knowledge naturally arises from prior experimental and observational data, among others.