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Bayesian Methods in Structural Bioinformatics

Overview of attention for book
Cover of 'Bayesian Methods in Structural Bioinformatics'

Table of Contents

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    Book Overview
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    Chapter 1 An Overview of Bayesian Inference and Graphical Models
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    Chapter 2 Monte Carlo Methods for Inference in High-Dimensional Systems
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    Chapter 3 On the Physical Relevance and Statistical Interpretation of Knowledge-Based Potentials
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    Chapter 4 Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method
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    Chapter 5 Inferring Knowledge Based Potentials Using Contrastive Divergence
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    Chapter 6 Statistics of Bivariate von Mises Distributions
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    Chapter 7 Statistical Modelling and Simulation Using the Fisher-Bingham Distribution
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    Chapter 8 Likelihood and Empirical Bayes Superposition of Multiple Macromolecular Structures
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    Chapter 9 Bayesian Hierarchical Alignment Methods
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    Chapter 10 Probabilistic Models of Local Biomolecular Structure and Their Applications
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    Chapter 11 Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields
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    Chapter 12 Inferential Structure Determination from NMR Data
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    Chapter 13 Bayesian Methods in SAXS and SANS Structure Determination
Attention for Chapter 2: Monte Carlo Methods for Inference in High-Dimensional Systems
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Chapter title
Monte Carlo Methods for Inference in High-Dimensional Systems
Chapter number 2
Book title
Bayesian Methods in Structural Bioinformatics
Published by
Springer, Berlin, Heidelberg, January 2012
DOI 10.1007/978-3-642-27225-7_2
Book ISBNs
978-3-64-227224-0, 978-3-64-227225-7
Authors

Jesper Ferkinghoff-Borg

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 33%
Researcher 1 33%
Student > Postgraduate 1 33%
Readers by discipline Count As %
Mathematics 1 33%
Biochemistry, Genetics and Molecular Biology 1 33%
Agricultural and Biological Sciences 1 33%

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