"The state of the art presented by the experts in the field."
-- Ross D. Shachter,
Department of Engineering-Economic Systems and Operations Research,
Stanford University
Graphical models, a marriage between probability theory and graph
theory, provide a natural tool for dealing with two problems that
occur throughout applied mathematics and engineering -- uncertainty and
complexity. In particular, they play an increasingly important role
in the design and analysis of machine learning algorithms.
Fundamental to the idea of a graphical model is the notion of
modularity: a complex system is built by combining simpler parts.
Probability theory serves as the glue whereby the parts are combined,
ensuring that the system as a whole is consistent and providing ways
to interface models to data. Graph theory provides both an
intuitively appealing interface by which humans can model highly
interacting sets of variables and a data structure that lends itself
naturally to the design of efficient general-purpose algorithms.
This book presents an in-depth exploration of issues related to
learning within the graphical model formalism. Four chapters are
tutorial chapters -- Robert Cowell on Inference for Bayesian Networks,
David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on
Variational Methods, and David Heckerman on Learning with Bayesian
Networks. The remaining chapters cover a wide range of topics of
current research interest.
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