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Learning in Graphical Models

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Cover of 'Learning in Graphical Models'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction to Inference for Bayesian Networks
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    Chapter 2 Advanced Inference in Bayesian Networks
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    Chapter 3 Inference in Bayesian Networks Using Nested Junction Trees
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    Chapter 4 Bucket Elimination: A Unifying Framework for Probabilistic Inference
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    Chapter 5 An Introduction to Variational Methods for Graphical Models
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    Chapter 6 Improving the Mean Field Approximation Via the Use of Mixture Distributions
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    Chapter 7 Introduction to Monte Carlo Methods
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    Chapter 8 Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation
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    Chapter 9 Chain Graphs and Symmetric Associations
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    Chapter 10 The Multiinformation Function as a Tool for Measuring Stochastic Dependence
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    Chapter 11 A Tutorial on Learning with Bayesian Networks
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    Chapter 12 A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
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    Chapter 13 Latent Variable Models
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    Chapter 14 Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization
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    Chapter 15 Learning Bayesian Networks with Local Structure
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    Chapter 16 Asymptotic Model Selection for Directed Networks with Hidden Variables
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    Chapter 17 A Hierarchical Community of Experts
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    Chapter 18 An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering
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    Chapter 19 Learning Hybrid Bayesian Networks from Data
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    Chapter 20 A Mean Field Learning Algorithm for Unsupervised Neural Networks
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    Chapter 21 Edge Exclusion Tests for Graphical Gaussian Models
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    Chapter 22 Hepatitis B: A Case Study in MCMC
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    Chapter 23 Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond
Attention for Chapter 12: A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
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Chapter title
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Chapter number 12
Book title
Learning in Graphical Models
Published by
Springer, Dordrecht, January 1998
DOI 10.1007/978-94-011-5014-9_12
Book ISBNs
978-9-40-106104-9, 978-9-40-115014-9
Authors

Radford M. Neal, Geoffrey E. Hinton, Neal, Radford M., Hinton, Geoffrey E.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 983 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 35 4%
Germany 13 1%
France 11 1%
United Kingdom 9 <1%
China 8 <1%
Japan 6 <1%
Canada 6 <1%
Switzerland 5 <1%
Spain 4 <1%
Other 30 3%
Unknown 856 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 326 33%
Researcher 181 18%
Student > Master 123 13%
Professor > Associate Professor 64 7%
Student > Bachelor 53 5%
Other 160 16%
Unknown 76 8%
Readers by discipline Count As %
Computer Science 439 45%
Engineering 173 18%
Mathematics 79 8%
Agricultural and Biological Sciences 46 5%
Physics and Astronomy 29 3%
Other 113 11%
Unknown 104 11%