Advanced Markov Chain Monte Carlo Methods: Learning from Past SamplesISBN: 9780470748268
378 pages
August 2010

Key Features:
 Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.
 A detailed discussion of the Monte Carlo MetropolisHastings algorithm that can be used for sampling from distributions with intractable normalizing constants.
 Uptodate accounts of recent developments of the Gibbs sampler.
 Comprehensive overviews of the populationbased MCMC algorithms and the MCMC algorithms with adaptive proposals.
This book can be used as a textbook or a reference book for a onesemester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.
Acknowledgments.
Publisher's Acknowledgments.
1 Bayesian Inference and Markov Chain Monte Carlo.
1.1 Bayes.
1.1.1 Specification of Bayesian Models.
1.1.2 The Jeffreys Priors and Beyond.
1.2 Bayes Output.
1.2.1 Credible Intervals and Regions.
1.2.2 Hypothesis Testing: Bayes Factors.
1.3 Monte Carlo Integration.
1.3.1 The Problem.
1.3.2 Monte Carlo Approximation.
1.3.3 Monte Carlo via Importance Sampling.
1.4 Random Variable Generation.
1.4.1 Direct or TransformationMethods.
1.4.2 AcceptanceRejection Methods.
1.4.3 The RatioofUniformsMethod and Beyond.
1.4.4 Adaptive Rejection Sampling.
1.4.5 Perfect Sampling.
1.5 Markov ChainMonte Carlo.
1.5.1 Markov Chains.
1.5.2 Convergence Results.
1.5.3 Convergence Diagnostics.
Exercises.
2 The Gibbs Sampler.
2.1 The Gibbs Sampler.
2.2 Data Augmentation.
2.3 Implementation Strategies and Acceleration Methods.
2.3.1 Blocking and Collapsing.
2.3.2 Hierarchical Centering and Reparameterization.
2.3.3 Parameter Expansion for Data Augmentation.
2.3.4 Alternating SubspaceSpanning Resampling.
2.4 Applications.
2.4.1 The StudenttModel.
2.4.2 Robit Regression or Binary Regression with the Studentt Link.
2.4.3 Linear Regression with IntervalCensored Responses.
Exercises.
Appendix 2A: The EMand PXEMAlgorithms.
3 The MetropolisHastings Algorithm.
3.1 TheMetropolisHastings Algorithm.
3.1.1 Independence Sampler.
3.1.2 RandomWalk Chains.
3.1.3 Problems withMetropolisHastings Simulations.
3.2 Variants of theMetropolisHastings Algorithm.
3.2.1 The HitandRun Algorithm.
3.2.2 The Langevin Algorithm.
3.2.3 TheMultipleTryMH Algorithm.
3.3 Reversible Jump MCMC Algorithm for Bayesian Model Selection Problems.
3.3.1 Reversible JumpMCMC Algorithm.
3.3.2 ChangePoint Identification.
3.4 MetropolisWithinGibbs Sampler for ChIPchip Data Analysis.
3.4.1 MetropolisWithinGibbs Sampler.
3.4.2 Bayesian Analysis for ChIPchip Data.
Exercises.
4 Auxiliary Variable MCMC Methods.
4.1 Simulated Annealing.
4.2 Simulated Tempering.
4.3 The Slice Sampler.
4.4 The SwendsenWang Algorithm.
4.5 TheWolff Algorithm.
4.6 The Mo/ller Algorithm.
4.7 The Exchange Algorithm.
4.8 The DoubleMH Sampler.
4.8.1 Spatial AutologisticModels.
4.9 Monte CarloMH Sampler.
4.9.1 Monte CarloMH Algorithm.
4.9.2 Convergence.
4.9.3 Spatial AutologisticModels (Revisited).
4.9.4 Marginal Inference.
4.10 Applications.
4.10.1 AutonormalModels.
4.10.2 Social Networks.
Exercises.
5 PopulationBased MCMC Methods.
5.1 Adaptive Direction Sampling.
5.2 Conjugate GradientMonte Carlo.
5.3 SampleMetropolisHastings Algorithm.
5.4 Parallel Tempering.
5.5 EvolutionaryMonte Carlo.
5.5.1 Evolutionary Monte Carlo in BinaryCoded Space.
5.5.2 EvolutionaryMonte Carlo in Continuous Space.
5.5.3 Implementation Issues.
5.5.4 Two Illustrative Examples.
5.5.5 Discussion.
5.6 Sequential Parallel Tempering for Simulation of High Dimensional Systems.
5.6.1 Buildup Ladder Construction.
5.6.2 Sequential Parallel Tempering.
5.6.3 An Illustrative Example: the Witch’s Hat Distribution.
5.6.4 Discussion.
5.7 EquiEnergy Sampler.
5.8 Applications.
5.8.1 Bayesian Curve Fitting.
5.8.2 Protein Folding Simulations: 2D HPModel.
5.8.3 Bayesian Neural Networks for Nonlinear Time Series Forecasting.
Exercises.
Appendix 5A: Protein Sequences for 2D HPModels.
6 Dynamic Weighting.
6.1 DynamicWeighting.
6.1.1 The IWIWPrinciple.
6.1.2 Tempering DynamicWeighting Algorithm.
6.1.3 DynamicWeighting in Optimization.
6.2 DynamicallyWeighted Importance Sampling.
6.2.1 The Basic Idea.
6.2.2 A Theory of DWIS.
6.2.3 Some IWIWp Transition Rules.
6.2.4 Two DWIS Schemes.
6.2.5 Weight Behavior Analysis.
6.2.6 A Numerical Example.
6.3 Monte Carlo Dynamically Weighted Importance Sampling.
6.3.1 Sampling from Distributions with Intractable Normalizing Constants.
6.3.2 Monte Carlo Dynamically Weighted Importance Sampling.
6.3.3 Bayesian Analysis for Spatial Autologistic Models.
6.4 Sequentially Dynamically Weighted Importance Sampling.
Exercises.
7 Stochastic Approximation Monte Carlo.
7.1 MulticanonicalMonte Carlo.
7.2 1/kEnsemble Sampling.
7.3 TheWangLandau Algorithm.
7.4 Stochastic ApproximationMonte Carlo.
7.5 Applications of Stochastic ApproximationMonte Carlo.
7.5.1 Efficient pValue Evaluation for ResamplingBased Tests.
7.5.2 Bayesian Phylogeny Inference.
7.5.3 Bayesian Network Learning.
7.6 Variants of Stochastic ApproximationMonte Carlo.
7.6.1 Smoothing SAMC forModel Selection Problems.
7.6.2 Continuous SAMC for Marginal Density Estimation.
7.6.3 Annealing SAMC for Global Optimization.
7.7 Theory of Stochastic ApproximationMonte Carlo.
7.7.1 Convergence.
7.7.2 Convergence Rate.
7.7.3 Ergodicity and its IWIWProperty.
7.8 Trajectory Averaging: Toward the Optimal Convergence Rate.
7.8.1 Trajectory Averaging for a SAMCMC Algorithm.
7.8.2 Trajectory Averaging for SAMC.
7.8.3 Proof of Theorems 7.8.2 and 7.8.3.
Exercises.
Appendix 7A: Test Functions for Global Optimization.
8 Markov Chain Monte Carlo with Adaptive Proposals.
8.1 Stochastic ApproximationBased Adaptive Algorithms.
8.1.1 Ergodicity andWeak Law of Large Numbers.
8.1.2 AdaptiveMetropolis Algorithms.
8.2 Adaptive IndependentMetropolisHastings Algorithms.
8.3 RegenerationBased Adaptive Algorithms.
8.3.1 Identification of Regeneration Times.
8.3.2 Proposal Adaptation at Regeneration Times.
8.4 PopulationBased Adaptive Algorithms.
8.4.1 ADS, EMC, NKC andMore.
8.4.2 Adaptive EMC.
8.4.3 Application to Sensor Placement Problems.
Exercises.
References.
Index.
“The book is suitable as a textbook for onesemester courses on Monte Carlo methods, offered at the advance postgraduate levels.” (Mathematical Reviews, 1 December 2012)
"Researchers working in the field of applied statistics will profit from this easytoaccess presentation. Further illustration is done by discussing interesting examples and relevant applications. The valuable reference list includes technical reports which are hard to and by searching in public data bases." (Zentralblatt MATH, 2011)"This book can be used as a textbook or a reference book for a onesemester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial." (Breitbart.com: Business Wire , 1 February 2011)
"The Markov Chain Monte Carlo method has now become the dominant methodology for solving many classes of computational problems in science and technology." (SciTech Book News, December 2010) 