Learning Dynamic Bayesian Networks
This paper presents a probabilistic framework for learning models of temporal data. We express these models using the Bayesian network formalism, a marriage of probability theory and graph theory in which dependencies between variables are expressed graphically. The graph not only allows the user to understand which variables affect which other ones, but also serves as the backbone for efficiently computing marginal and conditional probabilities that may be required for inference and learning.