Learning Bayesian Networks from Data
Statistics, Pattern Recognition and Information Theory There are many books on statistics. We find [DeGroot 1970] to be a good introduction to statistics and Bayesian statistics in particular. A more recent book [Gelman et al. 1995] is also a good introduction to this field and also discusses recent advances, such as hierarchical priors. Books in pattern recognition, including the classic [Duda and Hart 1973] and the more recent [Bishop 1995], cover basic issues in density estimation and their use for pattern recognition and classification. A good introduction to information theory, and notions such as KL divergence and mutual information can be found in [Cover and Thomas 1991].