Learning Bayesian Networks with Local Structure
AUTHOR: Nir Friedman
AFFILIATION: Stanford University
ABSTRACT:
{\em Bayesian networks\/} are arguably the method of choice in
artificial intelligence for representing and reasoning with
probabilistic knowledge. These networks exploit the structure of the
domain (in terms of conditional independences) to allow compact
representation of probability measures and efficient inference
procedures, and are currently used in numerous applications. A major
problem in practice is the aquisition of these networks, which can be
often expansive. Thus, there is a growing body of work on learning
Bayesian networks from raw data, which is often readily available. I
will start the talk with a quick overview of our current project on
learning probabilistic models from data.
I will then focus on a novel extension to the known methods for
learning Bayesian networks from data that improves the quality of the
learned models. Current methods learn a Bayesian network by searching
the space of network structures. The quantification of parameters in
each candidate structure is done by estimating the full {\em
conditional probability table\/} (CPT) for each variable. Our
approach explicitly represents and learns the {\em local structure\/}
in the CPTs. This increases the space of possible models, enabling the
representation of CPTs with a variable number of parameters that
depends on the learned local structures. The benefit is that the
learning routine is capable of inducing models that emulate better the
real complexity of the interactions observed in the data.
I will describe the theoretical foundation and practical aspects of
learning local structures, as well as an empirical evaluation of the
proposed method. This evaluation compare this approach with the
standard learning procedure. The resulting learning curves of the
procedure that exploits the local structure converge faster than these
of the standard procedure. Our results also show that networks learned
with local structure tend to be more complex (in terms of arcs), yet
maintain less parameters.
This talk describes joint work with Moises Goldszmidt of Rockwell
Science Center.