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Bayesian Methods in Structural Bioinformatics

Overview of attention for book
Bayesian Methods in Structural Bioinformatics
Springer Berlin Heidelberg

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
Overall attention for this book and its chapters
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7 X users
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3 Wikipedia pages

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78 Mendeley
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Title
Bayesian Methods in Structural Bioinformatics
Published by
Springer Berlin Heidelberg, March 2012
DOI 10.1007/978-3-642-27225-7
ISBNs
978-3-64-227224-0, 978-3-64-227225-7
Editors

Hamelryck, Thomas, Mardia, Kanti, Ferkinghoff-Borg, Jesper

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X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
France 1 1%
Unknown 76 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 12%
Researcher 4 5%
Lecturer 2 3%
Student > Bachelor 2 3%
Professor > Associate Professor 2 3%
Other 3 4%
Unknown 56 72%
Readers by discipline Count As %
Computer Science 5 6%
Agricultural and Biological Sciences 4 5%
Economics, Econometrics and Finance 4 5%
Mathematics 3 4%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 4 5%
Unknown 56 72%

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