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Learning in Graphical Models
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
other
Author(s):
Radford M. Neal
,
Geoffrey E. Hinton
Publication date
(Print):
1998
Publisher:
Springer Netherlands
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
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Music and Mental Imagery
Author and book information
Book Chapter
Publication date (Print):
1998
Pages
: 355-368
DOI:
10.1007/978-94-011-5014-9_12
SO-VID:
ace664c9-d18b-429d-adb1-dfc9130ae292
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Book chapters
pp. 75
Bucket Elimination: A Unifying Framework for Probabilistic Inference
pp. 105
An Introduction to Variational Methods for Graphical Models
pp. 163
Improving the Mean Field Approximation Via the Use of Mixture Distributions
pp. 175
Introduction to Monte Carlo Methods
pp. 205
Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation
pp. 261
The Multiinformation Function as a Tool for Measuring Stochastic Dependence
pp. 301
A Tutorial on Learning with Bayesian Networks
pp. 355
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
pp. 371
Latent Variable Models
pp. 421
Learning Bayesian Networks with Local Structure
pp. 495
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering
pp. 541
A Mean Field Learning Algorithm for Unsupervised Neural Networks
pp. 599
Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond
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