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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 7: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution
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