A large portion of real-world data is stored in commercial relational
database systems.  In contrast, most statistical learning methods work
only with "flat" data representations.  Thus, to apply these methods,
we are forced to convert the data into a flat form, thereby losing
much of the relational structure present in the data and potentially
introducing statistical skew.  These drawbacks severely limit the
ability of current methods to mine relational databases.

In this talk I will review recent work on probabilistic models,
including Bayesian networks (BNs) and Probabilistic Relational Models
(PRMs), and then describe the main contribution of my thesis: the
development of techniques for automatically inducing PRMs directly
from structured data stored in a relational or object-oriented
database.  These algorithms provide the necessary tools to discover
patterns in structured data, and provide new techniques for mining
relational data.  As we go along, I'll present experimental results in
several domains, including a biological domain describing tuberculosis
epidemiology, a database of scientific paper author and citation
information, and Web data.  Finally I will present an application of
these techniques to the task of selectivity estimation for database
query optimization.