Being Bayesian about Network Structure

Understand

In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other.

• Bayesian model-selection attempts to find the MAP model and use its structure to answer these questions.
• However, when the amount of available data is modest, there might be many models that have non-negligible posterior.
• Thus, we want compute the Bayesian posterior of a feature, i.e., the total posterior probability of all models that contain it.