Structure Learning and Model Selection Criteria
There are so many possible ways to learn the causality in a particular dataset. However, I think SOM is also an alternative…I will investigate this!
Also in autoregressive community, Granger causality plays quite an important role in time-series causal structure learning. There might be some connections between this Granger causality and other standard structure learnign algorithm in BN community.
One big problem for structure learning is the complexity of the data. Most of the time we would like to have the simplest model that works well for the dataset according to Occam’s razor. So we would like to have a function that penalizes the complexity. This problem is also referred as “model selection” problem. There are a lot of model selection criteria that I would like to investigate in order to use with Bayesian network structure learning. Here are some possible criteria:
AIC, BIC, CIC, MDL, NML, factorized NML