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Bayesian Methods in Structural Bioinformatics / edited by Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Statistics for Biology and Health | SpringerLink BücherPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012Description: Online-Ressource (XXII, 385p. 86 illus., 7 illus. in color, digital)ISBN:- 9783642272257
- 572.86
- 519.5
- QA276-280
Contents:
Summary: Part I Foundations: An Overview of Bayesian Inference and Graphical Models -- Monte Carlo Methods for Inferences in High-dimensional Systems -- Part II Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials -- Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures -- A Statistical View on the Reference Ratio Method -- Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution -- Statistics of Bivariate von Mises Distributions -- Bayesian Hierarchical Alignment Methods -- Likelihood and Empirical Bayes Superpositions of Multiple Macromolecular Structures -- Part IV Graphical models for structure prediction: Probabilistic Models of Local Biomolecular Structure and their Application in Structural Simulation -- Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields -- Part V Inferring Structure from Experimental Data -- Inferential Structure Determination from NMR Data -- Bayesian Methods in SAXS and SANS Structure Determination.Summary: This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.PPN: PPN: 1651398615Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXMS | ZDB-2-SMA
Bayesian Methods in Structural Bioinformatics; Foreword; Preface; Acknowledgements; Contents; Contributors; Acronyms; Part I Foundations; 1 An Overview of Bayesian Inference and Graphical Models; 1.1 Introduction; 1.2 Historical Background; 1.3 The Bayesian Probability Calculus; 1.3.1 Overview; 1.3.2 Full Bayesian Approach; 1.3.3 Example: The Binomial Distribution; 1.3.4 Hierarchical Models and Nuisance Variables; 1.3.5 Point Estimates; 1.3.5.1 MAP and ML Estimation; 1.3.5.2 Asymptotic Properties; 1.3.5.3 Empirical Bayes and Shrinkage Estimators; 1.3.5.4 Pseudolikelihood and Moment Estimation
1.4 Foundational Arguments1.4.1 The Cox Axioms; 1.4.2 Other Arguments; 1.5 Information Theory; 1.5.1 Information Entropy; 1.5.2 Kullback-Leibler Divergence; 1.5.3 Mutual Information; 1.6 Prior Distributions; 1.6.1 Principles for the Construction of Priors; 1.6.1.1 Principle of Indifference; 1.6.1.2 Maximum Entropy; 1.6.1.3 Jeffreys' Prior; 1.6.1.4 Reference Priors; 1.6.2 Conjugate Priors; 1.6.3 Improper Priors; 1.7 Model Selection; 1.7.1 Bayes Factor; 1.7.2 Bayesian Information Criterion; 1.7.3 Akaike Information Criterion; 1.7.4 Deviance Information Criterion; 1.7.5 Reversible Jump MCMC
1.8 Statistical Mechanics1.8.1 Overview; 1.8.2 Boltzmann Distribution; 1.8.3 Free Energy; 1.9 Graphical Models; 1.9.1 Introduction; 1.9.2 Graphical Models: Main Ideas; 1.9.3 Bayesian Networks; 1.9.3.1 General Properties; 1.9.3.2 Dynamic Bayesian Networks; 1.9.3.3 Ancestral Sampling; 1.9.4 Markov Random Fields; 1.9.5 Factor Graphs; 1.9.6 Inference for Graphical Models; 1.9.6.1 Message-Passing Algorithms; 1.9.6.2 Monte Carlo Methods; 1.9.7 Learning for Graphical Models; 1.10 Conclusions; 1.11 Further Reading; 2 Monte Carlo Methods for Inference in High-DimensionalSystems; 2.1 Introduction
2.2 Probabilities and Partition Functions2.2.1 Reference Distributions and Measures; 2.2.2 Inference and the Curse of Dimensionality; 2.3 Importance Sampling; 2.3.1 Sampling from a Target-Approximated Distribution; 2.3.2 Population Monte Carlo; 2.4 Markov Chain Monte Carlo; 2.4.1 Metropolis-Hastings Algorithm; 2.4.2 Gibbs Sampling; 2.4.3 Continuous State Space; 2.5 Estimators; 2.5.1 Expectation Values; 2.5.2 Histogram Method; 2.5.3 Ratio of Partition Functions; 2.5.4 Limitations of the Standard MCMC-Estimators; 2.6 Ergodicity and Convergence; 2.6.1 Event-Driven Simulation
2.6.2 Efficient Proposal Distributions2.7 Extended Ensembles; 2.7.1 Parallel Tempering; 2.7.2 Simulated Tempering; 2.7.3 Multicanonical Ensemble; 2.7.4 1/k-ensemble; 2.7.5 Extensions along Reaction Coordinates; 2.7.6 Sampling Times and Efficiency Optimization; 2.8 Learning Aspects of Generalized Ensembles; 2.8.1 Single Histogram Method; 2.8.2 Transition Based Methods; 2.8.3 Hybrid Methods; 2.8.4 Wang-Landau Method; 2.8.5 Metadynamics; 2.8.6 Muninn: An Automated Method Based on Multi-histogram Equations; 2.8.6.1 Generalized Multihistogram Equations; 2.8.6.2 Iteration Scheme
2.8.6.3 Resolution Scheme
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