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Home > Learning in Graphical Models > Introduction to Inference for Bayesian Networks
Learning in Graphical Models |
M.I. Jordan
Springer Netherlands
Learning in Graphical Models
Introduction to Inference for Bayesian Networks
Authors

Robert Cowell

ARI Id

1664958197565_4233062

Access

Not Available Free

Pages

9-26

DOI

10.1007/978-94-011-5014-9_1

Chapter URL

https://rd.springer.com/chapter/10.1007/978-94-011-5014-9_1

Table of Contents of Book
Showing 1 to 20 of 29 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
i-5
Front Matter
7-7
Introduction to Inference for Bayesian Networks
Robert Cowell
9-26
Advanced Inference in Bayesian Networks
Robert Cowell
27-49
Inference in Bayesian Networks Using Nested Junction Trees
Uffe Kjærulff
51-74
Bucket Elimination: A Unifying Framework for Probabilistic Inference
R. Dechter
75-104
An Introduction to Variational Methods for Graphical Models
Michael I. Jordan; Zoubin Ghahramani; Tommi S. Jaakkola; Lawrence K. Saul
105-161
Improving the Mean Field Approximation Via the Use of Mixture Distributions
Tommi S. Jaakkola; Michael I. Jordan
163-173
Introduction to Monte Carlo Methods
D. J. C. Mackay
175-204
Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation
Radford M. Neal
205-228
Front Matter
229-229
Chain Graphs and Symmetric Associations
Thomas S. Richardson
231-259
The Multiinformation Function as a Tool for Measuring Stochastic Dependence
M. Studený; J. Vejnarová
261-297
Front Matter
299-299
A Tutorial on Learning with Bayesian Networks
David Heckerman
301-354
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Radford M. Neal; Geoffrey E. Hinton
355-368
Front Matter
369-369
Latent Variable Models
Christopher M. Bishop
371-403
Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization
Joachim M. Buhmann
405-419
Learning Bayesian Networks with Local Structure
Nir Friedman; Moises Goldszmidt
421-459
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 29 entries
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