Handbook of Markov Chain Monte Carlo

Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng Spatial point processes, Mark Huber The data augmentation algorithm: Theory and methodology, James P. Hobert Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox Statistical ecology, Ruth King Gaussian random field models for spatial data, Murali Haran Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger MCMC for state space models, Paul Fearnhead MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens Applications of MCMC in fisheries science, Russell B. Millar Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Western

[1]  Geoff K. Nicholls,et al.  Perfect simulation for sample-based inference , 1999 .

[2]  Adrian F. M. Smith,et al.  Automatic Bayesian curve fitting , 1998 .

[3]  C. Geyer,et al.  Simulation Procedures and Likelihood Inference for Spatial Point Processes , 1994 .

[4]  Jayanta K. Ghosh,et al.  Model selection - An overview , 2001 .

[5]  Nando de Freitas,et al.  Reversible Jump MCMC Simulated Annealing for Neural Networks , 2000, UAI.

[6]  S. Godsill.  On the Relationship Between Markov chain Monte Carlo Methods for Model Uncertainty , 2001 .

[7]  I ScottKirkpatrick.  Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[8]  David J Nott,et al.  Sampling Schemes for Bayesian Variable Selection in Generalized Linear Models , 2004 .

[9]  Robert B. Gramacy,et al.  Importance tempering , 2007, Stat. Comput..

[10]  J. Propp,et al.  Exact sampling with coupled Markov chains and applications to statistical mechanics , 1996 .

[11]  Miquel Trias,et al.  Delayed rejection schemes for efficient Markov-Chain Monte-Carlo sampling of multimodal distributions , 2009, 0904.2207.

[12]  Stephen G. Walker.  A Gibbs Sampling Alternative to Reversible Jump MCMC , 2009 .

[13]  A. Doucet,et al.  Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes , 2004, Journal of Time Series Analysis.

[14]  Stephen P. Brooks,et al.  Perfect Forward Simulation via Simulated Tempering , 2006 .

[15]  C. Geyer.  On Non-reversible Markov Chains , 2000 .

[16]  Edward I. George,et al.  The Practical Implementation of Bayesian Model Selection , 2001 .

[17]  Robert West,et al.  Generalised Additive Models , 2012 .

[18]  S. Brooks,et al.  Classical model selection via simulated annealing , 2003, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[19]  Peter Green,et al.  A primer in Markov Chain Monte Carlo , 2001 .

[20]  Yanan Fan,et al.  Bayesian modelling of prehistoric corbelled domes , 2000 .

[21]  P. Dellaportas,et al.  Bayesian variable and link determination for generalised linear models , 2003 .

[22]  Petros Dellaportas,et al.  Bayesian Variable and Link Determination for Generalised , 2000 .

[23]  L. Tierney.  A note on Metropolis-Hastings kernels for general state spaces , 1998 .

[24]  A. Jasra,et al.  Population-based reversible jump Markov chain , 2005 .

[25]  M. Vannucci,et al.  Bayesian Variable Selection in Clustering High-Dimensional Data , 2005 .

[26]  B. Carlin,et al.  Diagnostics: A Comparative Review , 2022 .

[27]  Petros Dellaportas,et al.  On Bayesian model and variable selection using MCMC , 2002, Stat. Comput..

[28]  S. Frühwirth-Schnatter.  Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models , 2001 .

[29]  Radford M. Neal.  Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better , 2004, math/0407281.

[30]  Peter J. Diggle,et al.  Statistical analysis of spatial point patterns , 1983 .

[31]  C. Robert,et al.  Computational and Inferential Difficulties with Mixture Posterior Distributions , 2000 .

[32]  Cong Han,et al.  MCMC Methods for Computing Bayes Factors: A Comparative Review , 2000 .

[33]  Christian P. Robert,et al.  Reversible Jump MCMC Converging to Birth-and-Death MCMC and More General Continuous Time Samplers , 2001 .

[34]  P. Green,et al.  Delayed rejection in reversible jump Metropolis–Hastings , 2001 .

[35]  Tim Hesterberg,et al.  Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.

[36]  J. Berger,et al.  Training samples in objective Bayesian model selection , 2004, math/0406460.

[37]  Petros Dellaportas,et al.  Multivariate mixtures of normals with unknown number of components , 2006, Stat. Comput..

[38]  W. Wong,et al.  Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models , 2001 .

[39]  M. Stephens.  Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods , 2000 .

[40]  P. Green,et al.  Trans-dimensional Markov chain Monte Carlo , 2000 .

[41]  Yanan Fan,et al.  Towards automating model selection for a mark–recapture–recovery analysis , 2009 .

[42]  Christian P. Robert,et al.  MCMC Convergence Diagnostics : A « Reviewww » , 1998 .

[43]  R. Kohn,et al.  Nonparametric regression using Bayesian variable selection , 1996 .

[44]  G. Roberts,et al.  Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions , 2003 .

[45]  L Tierney,et al.  Some adaptive monte carlo methods for Bayesian inference. , 1999, Statistics in medicine.

[46]  Petar M. Djuric,et al.  Model selection by MCMC computation , 2001, Signal Process..

[47]  M. Hurn,et al.  Improving the acceptance rate of reversible jump MCMC proposals , 2004 .

[48]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[49]  Luca Tardella,et al.  A geometric approach to transdimensional MCMC , 2003 .

[50]  Petros Dellaportas,et al.  A general proposal construction for reversible jump MCMC , 2009 .

[51]  C. Robert,et al.  Estimating Mixtures of Regressions , 2003 .

[52]  Simon J. Godsill,et al.  Discussion of `Trans-dimensional Markov chain Monte Carlo' , 2003 .

[53]  B. Ripley.  Modelling Spatial Patterns , 1977 .

[54]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[55]  P. Green,et al.  Bayesian Variable Selection and the Swendsen-Wang Algorithm , 2004 .

[56]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[57]  James O. Berger,et al.  Objective Bayesian Methods for Model Selection: Introduction and Comparison , 2001 .

[58]  Walter R. Gilks,et al.  Bayesian model comparison via jump diffusions , 1995 .

[59]  G. Roberts,et al.  Adaptive Markov Chain Monte Carlo through Regeneration , 1998 .

[60]  A. Mira,et al.  Efficient Bayes factor estimation from the reversible jump output , 2006 .

[61]  A. P. Dawid,et al.  Bayesian Model Averaging and Model Search Strategies , 2007 .

[62]  Ajay Jasra,et al.  Population-Based Reversible Jump Markov Chain Monte Carlo , 2007, 0711.0186.

[63]  Scott A. Sisson,et al.  Trans-dimensional Markov chains : A decade of progress and future perspectives , 2004 .

[64]  Zhihui Liu,et al.  BAYESIAN MIXTURE· MODELS , 2010 .

[65]  Gareth O. Roberts,et al.  Examples of Adaptive MCMC , 2009 .

[66]  R. Kass,et al.  Bayesian curve-fitting with free-knot splines , 2001 .

[67]  A. George,et al.  A Bayesian Approach to Ordering Gene Markers , 1999, Biometrics.

[68]  M. Stephens.  Dealing with label switching in mixture models , 2000 .

[69]  Gareth W. Peters,et al.  Automating and evaluating reversible jump MCMC proposal distributions , 2009, Stat. Comput..

[70]  Stephen P. Brooks,et al.  Adaptive Proposal Construction for Reversible Jump MCMC , 2008 .

[71]  Xiao-Li Meng,et al.  SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION , 1996 .

[72]  Stephen P. Brooks,et al.  Markov chain Monte Carlo method and its application , 1998 .

[73]  E. George,et al.  Journal of the American Statistical Association is currently published by American Statistical Association. , 2007 .

[74]  B. Carlin,et al.  Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .

[75]  Colin Fox,et al.  Posterior Exploration for Computationally Intensive Forward Models , 2011 .

[76]  Darryn Bryant,et al.  A Generalized Markov Sampler , 2004 .

[77]  Christian P. Robert,et al.  Linking theory and practice of MCMC. , 2003 .

[78]  S A Sisson,et al.  Bayesian Point Estimation of Quantitative Trait Loci , 2004, Biometrics.

[79]  C. Preston.  Spatial birth and death processes , 1975, Advances in Applied Probability.

[80]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[81]  Michael I. Miller,et al.  REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .

[82]  Faming Liang,et al.  A Theory for Dynamic Weighting in Monte Carlo Computation , 2001 .

[83]  P. Peskun,et al.  Optimum Monte-Carlo sampling using Markov chains , 1973 .

[84]  J. Kadane,et al.  Identification of Regeneration Times in MCMC Simulation, With Application to Adaptive Schemes , 2005 .

[85]  H. Haario,et al.  An adaptive Metropolis algorithm , 2001 .

[86]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[87]  Michael I. Miller,et al.  Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition , 1995, IEEE Trans. Signal Process..

[88]  Radford M. Neal,et al.  ANALYSIS OF A NONREVERSIBLE MARKOV CHAIN SAMPLER , 2000 .

[89]  J. Berger,et al.  Optimal predictive model selection , 2004, math/0406464.

[90]  C. Geyer,et al.  Annealing Markov chain Monte Carlo with applications to ancestral inference , 1995 .

[91]  Arnaud Doucet,et al.  Interacting sequential Monte Carlo samplers for trans-dimensional simulation , 2008, Comput. Stat. Data Anal..

[92]  Paolo Giudici,et al.  Nonparametric Convergence Assessment for MCMC Model Selection , 2003 .

[93]  Ruth King,et al.  A Classical Study of Catch-Effort Models for Hector's Dolphins , 2004 .

[94]  Scott A. Sisson,et al.  A distance-based diagnostic for trans-dimensional Markov chains , 2007, Stat. Comput..