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Federico Becca , Sandro Sorella
出版情報: Cambridge Core  1 online resource (xii, 274 p.)
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Iain D. Boyd, University of Michigan, Thomas E. Schwartzentruber, University of Minnesota
出版情報:   1 online resource (xxi, 360 p.)
シリーズ名: Cambridge aerospace series ; 42
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Richard M. Martin, Lucia Reining, David M. Ceperley
出版情報:   1 online resource (xxiv, 818 p.)
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J.E. Gubernatis, N. Kawashima, P. Werner
出版情報:   1 online resource (xiii, 488 p.)
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Faming Liang, Chuanhai Liu, Raymond J. Carroll
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (xix, 357 p.)
シリーズ名: Wiley series in computational statistics
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目次情報: 続きを見る
Preface
Acknowledgements
List of Figures
List of Tables
Bayesian Inference and Markov chain Monte Carlo / 1:
Bayes / 1.1:
Bayes output / 1.2:
Monte Carlo Integration / 1.3:
Random variable generation / 1.4:
Markov chain Monte Carlo / 1.5:
Exercises
The Gibbs sampler / 2:
Data Augmentation / 2.1:
Implementation strategies and acceleration methods / 2.3:
Applications / 2.4:
The Metropolis-Hastings Algorithm / 3:
Some Variants of the Metropolis-Hastings Algorithm / 3.1:
Reversible Jump MCMC Algorithm for Bayesian Model Selection / 3.3:
Problems
Metropolis-within-Gibbs Sampler for ChIP-chip Data Analysis / 3.4:
Auxiliary Variable MCMC Methods / 4:
Simulated Annealing / 4.1:
Simulated Tempering / 4.2:
Slice Sampler / 4.3:
The Swendsen-Wang Algorithm / 4.4:
The Wolff Algorithm / 4.5:
The Mller algorithm / 4.6:
The Exchange Algorithm / 4.7:
Double MH Sampler / 4.8:
Monte Carlo MH Sampler / 4.9:
Population-Based MCMC Methods / 4.10:
Adaptive Direction Sampling / 5.1:
Conjugate Gradient Monte Carlo / 5.2:
Sample Metropolis-Hastings Algorithm / 5.3:
Parallel Tempering / 5.4:
Evolutionary Monte Carlo / 5.5:
Sequential Parallel Tempering for Simulation of High Dimensional / 5.6:
Systems
Equi-Energy Sampler / 5.7:
Forecasting / 5.8:
Dynamic Weighting / 6:
Dynamically Weighted Importance Sampling / 6.1:
Monte Carlo Dynamically Weighted Importance Sampling / 6.3:
Sequentially Dynamically Weighted Importance Sampling / 6.4:
Stochastic Approximation Monte Carlo / 7:
Multicanonical Monte Carlo / 7.1:
1/k-Ensemble Sampling / 7.2:
Wang-Landau Algorithm / 7.3:
Applications of Stochastic Approximation Monte Carlo / 7.4:
Variants of Stochastic Approximation Monte Carlo / 7.6:
Theory of Stochastic Approximation Monte Carlo / 7.7:
Trajectory Averaging: Toward the Optimal Convergence Rate / 7.8:
Markov Chain Monte Carlo with Adaptive Proposals / 8:
Stochastic Approximation-based Adaptive Algorithms / 8.1:
Adaptive Independent Metropolis-Hastings Algorithms / 8.2:
Regeneration-based Adaptive Algorithms / 8.3:
Population-based Adaptive Algorithms / 8.4:
References
Index
Acknowledgments
Publisher's Acknowledgments
Bayesian Inference and Markov Chain Monte Carlo
Specification of Bayesian Models / 1.1.1:
The Jeffreys Priors and Beyond / 1.1.2:
Bayes Output
Credible Intervals and Regions / 1.2.1:
Hypothesis Testing: Bayes Factors / 1.2.2:
The Problem / 1.3.1:
Monte Carlo Approximation / 1.3.2:
Monte Carlo via Importance Sampling / 1.3.3:
Random Variable Generation
Direct or Transformation Methods / 1.4.1:
Acceptance-Rejection Methods / 1.4.2:
The Ratio-of-Uniforms Method and Beyond / 1.4.3:
Adaptive Rejection Sampling / 1.4.4:
Perfect Sampling / 1.4.5:
Markov Chain Monte Carlo
Markov Chains / 1.5.1:
Convergence Results / 1.5.2:
Convergence Diagnostics / 1.5.3:
The Gibbs Sampler
Implementation Strategies and Acceleration Methods
Blocking and Collapsing / 2.3.1:
Hierarchical Centering and Reparameterization / 2.3.2:
Parameter Expansion for Data Augmentation / 2.3.3:
Alternating Subspace-Spanning Resampling / 2.3.4:
The Student-t Model / 2.4.1:
Robit Regression or Binary Regression with the Student-t Link / 2.4.2:
Linear Regression with Interval-Censored Responses / 2.4.3:
The EM and PX-EM Algorithms / Appendix 2A:
Independence Sampler / 3.1.1:
Random Walk Chains / 3.1.2:
Problems with Metropolis-Hastings Simulations / 3.1.3:
Variants of the Metropolis-Hastings Algorithm
The Hit-and-Run Algorithm / 3.2.1:
The Langevin Algorithm / 3.2.2:
The Multiple-Try MH Algorithm / 3.2.3:
Reversible Jump MCMC Algorithm for Bayesian Model Selection Problems
Reversible Jump MCMC Algorithm / 3.3.1:
Change-Point Identification / 3.3.2:
Metropolis-Within-Gibbs Sampler for ChIP-chip Data Analysis
Metropolis-Within-Gibbs Sampler / 3.4.1:
Bayesian Analysis for ChIP-chip Data / 3.4.2:
The Slice Sampler
The Møller Algorithm
The Double MH Sampler
Spatial Autologistic Models / 4.8.1:
Monte Carlo MH Algorithm / 4.9.1:
Convergence / 4.9.2:
Spatial Autologistic Models (Revisited) / 4.9.3:
Marginal Inference / 4.9.4:
Autonormal Models / 4.10.1:
Social Networks / 4.10.2:
Evolutionary Monte Carlo in Binary-Coded Space / 5.5.1:
Evolutionary Monte Carlo in Continuous Space / 5.5.2:
Implementation Issues / 5.5.3:
Two Illustrative Examples / 5.5.4:
Discussion / 5.5.5:
Sequential Parallel Tempering for Simulation of High Dimensional Systems
Build-up Ladder Construction / 5.6.1:
Sequential Parallel Tempering / 5.6.2:
An Illustrative Example: the Witch's Hat Distribution / 5.6.3:
Bayesian Curve Fitting / 5.6.4:
Protein Folding Simulations: 2D HP Model / 5.8.2:
Bayesian Neural Networks for Nonlinear Time Series Forecasting / 5.8.3:
Protein Sequences for 2D HP Models / Appendix 5A:
The IWIW Principle / 6.1.1:
Tempering Dynamic Weighting Algorithm / 6.1.2:
Dynamic Weighting in Optimization / 6.1.3:
The Basic Idea / 6.2.1:
A Theory of DWIS / 6.2.2:
Two DWIS Schemes / 6.2.3:
Weight Behavior Analysis / 6.2.5:
A Numerical Example / 6.2.6:
Sampling from Distributions with Intractable Normalizing Constants / 6.3.1:
Bayesian Analysis for Spatial Autologistic Models / 6.3.2:
The Wang-Landau Algorithm
Efficient p-Value Evaluation for Resampling-Based Tests / 7.5.1:
Bayesian Phylogeny Inference / 7.5.2:
Bayesian Network Learning / 7.5.3:
Smoothing SAMC for Model Selection Problems / 7.6.1:
Continuous SAMC for Marginal Density Estimation / 7.6.2:
Annealing SAMC for Global Optimization / 7.6.3:
Convergence Rate / 7.7.1:
Ergodicity and its IWIW Property / 7.7.3:
Trajectory Averaging for a SAMCMC Algorithm / 7.8.1:
Trajectory Averaging for SAMC / 7.8.2:
Proof of Theorems 7.8.2 and 7.8.3 / 7.8.3:
Test Functions for Global Optimization / Appendix 7At:
Stochastic Approximation-Based Adaptive Algorithms
Ergodicity and Weak Law of Large Numbers / 8.1.1:
Adaptive Metropolis Algorithms / 8.1.2:
Regeneration-Based Adaptive Algorithms
Identification of Regeneration Times / 8.3.1:
Proposal Adaptation at Regeneration Times / 8.3.2:
Population-Based Adaptive Algorithms
ADS, EMC, NKC and More / 8.4.1:
Adaptive.EMC / 8.4.2:
Application to Sensor Placement Problems / 8.4.3:
Preface
Acknowledgements
List of Figures
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電子ブック

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David P. Landau, Center for Simulational Physics, University of Georgia, USA, Kurt Binder, Institut f�ur Physik, Johannes-Gutenberg-Universit�at, Germany
出版情報:   1 online resource (xviii, 564 pages)
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電子ブック

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Keith P. Johnson, editor, Johannes M.L. Penninger, editor
出版情報: [S.l.] : ACS Publications  1 online resource (x, 550 p.)
シリーズ名: ACS symposium series ; 406
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目次情報: 続きを見る
New Directions in Supercritical Fluid Science and Technology
Fluorescence Spectroscopy Studies of Intermolecular Interactions in Supercritical Fluids
Solvation Structures in Supercritical Fluid Mixtures Based on Molecular
Distribution Functions
Gibbs-Ensemble Monte Carlo Simulations of Phase Equilibria in Supercritical
Fluid Mixtures
Spectroscopic Determination of Solvent Strength and Structure in Supercritical
Fluid Mixtures: A Review
Partition Coefficients of Poly(ethylene glycol)s in Supercritical Carbon Dioxide
Experimental Measurement of Supercritical Fluid-Liquid Phase Equilibrium
Vapor-Liquid Equilibria of Fatty Acid Esters in Supercritical Fluids
Four-Phase Equilibrium of Two Ternary Organic Systems with Carbon Dioxide
Direct Viscosity Enhancement of Carbon Dioxide
Pressure Tuning of Reverse Micelles for Adjustable Solvation of Hydrophiles in Supercritical Fluids
Structure of Reverse Micelle and Microemulsion Phases in Near-Critical and
Supercritical Fluid as Determined from Dynamic Light-Scattering Studies
Inverse Emulsion Polymerization of Acrylamide in Near-Critical and Supercritical
Continuous Phases
Interaction of Polymers with Near-Critical Carbon Dioxide
Kinetic Elucidation of the Acid-Catalyzed Mechanism of 1-Propanol Dehydration in
Supercritical Water
Chemistry of Metholynaphthalene in Supercritical Water
Fundamental Kinetics of Methonol Oxidation
Thermodynamic Analysis of Corrosion of Iron Alloys in Supercritical Water
Electrochemical Measurements of Corrosion of Iron Alloys in Supercritical Water
Phase and Reaction Equilibria Considerations in the Evaluation and Operation of
Supercritical Fluid Reaction Processes
Kinetic Model for Supercritical-Delignification of Wood
Gas Antisolvent Recrystallization: New Process to Recrystallize Compounds
Insoluble in Supercritical Fluids
Solids Formation After the Expansion of Supercritical Mixtures
Solid-Fluid Mass Transfer in a Packed Bed Under Supercritical Conditions
Two-Phase Heat Transfer in the Vicinity of a Lower Consolute Point
Extraction and Isolation of Chemotherapeutic Pyrrolizidine Alkaloids from Plant
Substrates: Novel Process Using Supercritical Fluids
Supercritical Fluid Carbon Dioxide Extraction in the Synthesis of
Trieicosapentaenoylglycerol from Fish Oil
Supercritical Carbon Dioxide Extraction of Lipids from Algae
Supercritical Extraction of Pollutants from Water and Soil
Current State of Extraction of Natural Materials with Supercritical Fluids and Developmental Trends
Design, Construction, and Operation of a Multipurpose Plant for Commercial
Supercritical Gas Extraction
Supercritical Fluid Extraction of Flavoring Material: Design and Economics
Selection of Components for Commercial Supercritical Fluid Food Processing
Plants
New Directions in Supercritical Fluid Science and Technology
Fluorescence Spectroscopy Studies of Intermolecular Interactions in Supercritical Fluids
Solvation Structures in Supercritical Fluid Mixtures Based on Molecular
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