Learning in Graphical Models: [proceedings of the NATO Advanced Study Institute... : Ettore Mairona Center, Erice, Italy, September 27-October 7, 1996]

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M.I. Jordan
Springer Science & Business Media, Mar 31, 1998 - Computers - 630 pages
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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.
  

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Contents

ADVANCED INFERENCE IN BAYESIAN NETWORKS
27
INFERENCE IN BAYESIAN NETWORKS USING NESTED JUNCTION TREES
51
A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE
75
AN INTRODUCTION TO VARIATIONAL METHODS FOR GRAPHICAL MODELS
105
IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS
163
INTRODUCTION TO MONTE CARLO METHODS
175
SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION
205
CHAIN GRAPHS AND SYMMETRIC ASSOCIATIONS
231
DATA CLUSTERING AND DATA VISUALIZATION
405
LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE
421
ASYMPTOTIC MODEL SELECTION FOR DIRECTED NETWORKS WITH HIDDEN VARIABLES
461
A HIERARCHICAL COMMUNITY OF EXPERTS
479
AN INFORMATIONTHEORETIC ANALYSIS OF HARD AND SOFT ASSIGNMENT METHODS FOR CLUSTERING
495
LEARNING HYBRID BAYESIAN NETWORKS FROM DATA
521
A MEAN FIELD LEARNING ALGORITHM FOR UNSUPERVISED NEURAL NETWORKS
541
EDGE EXCLUSION TESTS FOR GRAPHICAL GAUSSIAN MODELS
555

THE MULTIINFORMATION FUNCTION AS A TOOL FOR MEASURING STOCHASTIC DEPENDENCE
261
A TUTORIAL ON LEARNING WITH BAYESIAN NETWORKS
301
A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL SPARSE AND OTHER VARIANTS
355
LATENT VARIABLE MODELS
371
A CASE STUDY IN MCMC
575
FROM LINEAR REGRESSION TO LINEAR PREDICTION AND BEYOND
599
INDEX
623
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About the author (1998)

Jordan is Professor in the Department of Brain andCognitive Science at the Massachusetts Institute of Technology.

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