In the past decade, a number of different research communities
within the computational sciences have studied learning in
networks, starting from a number of different points of view. There
has been substantial progress in these different communities and
surprising convergence has developed between the formalisms. The
awareness of this convergence and the growing interest of
researchers in understanding the essential unity of the subject
underlies the current volume. Two research communities which have
used graphical or network formalisms to particular advantage are
the belief network community and the neural network community.
Belief networks arose within computer science and statistics and
were developed with an emphasis on prior knowledge and exact
probabilistic calculations. Neural networks arose within electrical
engineering, physics and neuroscience and have emphasised pattern
recognition and systems modelling problems. This volume draws
together researchers from these two communities and presents both
kinds of networks as instances of a general unified graphical
formalism. The book focuses on probabilistic methods for learning
and inference in graphical models, algorithm analysis and design,
theory and applications. Exact methods, sampling methods and
variational methods are discussed in detail. Audience: A wide
cross-section of computationally oriented researchers, including
computer scientists, statisticians, electrical engineers,
physicists and neuroscientists.
General
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