Trans-dimensional Markov chains : A decade of progress and future perspectives

The last ten years have witnessed the development of sampling frameworks that permit the construction of Markov chains which simultaneously traverse both parameter and model space. In this time substantial methodological progress has been made. In this article we present a survey of the current state of the art and evaluate some of the most recent advances in this field. We also discuss future research perspectives in the context of the drive to develop sampling mechanisms with high degrees of both efficiency and automation.

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

[2]  Francesco Bartolucci,et al.  Efficient estimate of Bayes factors from Reversible Jump output , 2003 .

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

[4]  G. Nicholls,et al.  Bridge estimation of the probability density at a point , 2001 .

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

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

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

[8]  Ming-Hui Chen,et al.  ESTIMATING RATIOS OF NORMALIZING CONSTANTS FOR DENSITIES WITH DIFFERENT DIMENSIONS , 1997 .

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

[10]  A. O'Hagan,et al.  Fractional Bayes factors for model comparison , 1995 .

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

[12]  Zhuowen Tu,et al.  Range image segmentation by an effective jump-diffusion method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Walter R. Gilks,et al.  Introduction to general state-space Markov chain theory , 1995 .

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

[15]  A. Doucet,et al.  Computational Advances for and from Bayesian Analysis , 2004 .

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

[17]  R. Waagepetersen,et al.  A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL‐mapping , 2001 .

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

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

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

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

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

[23]  Heikki Mannila,et al.  Genome segmentation using piecewise constant intensity models and reversible jump MCMC , 2002, ECCB.

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

[25]  S. Chib Marginal Likelihood from the Gibbs Output , 1995 .

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

[27]  David A. Stephens,et al.  BAYESIAN ANALYSIS OF QUANTITATIVE TRAIT LOCUS DATA USING REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO , 1998 .

[28]  James Allen Fill,et al.  An interruptible algorithm for perfect sampling via Markov chains , 1997, STOC '97.

[29]  Walter R. Gilks,et al.  BUGS - Bayesian inference Using Gibbs Sampling Version 0.50 , 1995 .

[30]  Christian P. Robert,et al.  The Bayesian choice , 1994 .

[31]  Xeni K. Dimakos,et al.  A Guide to Exact Simulation , 2001 .

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

[33]  O. Cappé,et al.  Markov Chain Monte Carlo: 10 Years and Still Running! , 2000 .

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

[35]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

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

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

[38]  Michael P. Wiper,et al.  Using weibull mixture distributions to model heterogeneous survival data , 2005 .

[39]  P. Royston,et al.  Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. , 1994 .

[40]  G. Casella,et al.  Explaining the Perfect Sampler , 2001 .

[41]  J. Berger,et al.  The Intrinsic Bayes Factor for Model Selection and Prediction , 1996 .

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

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

[44]  D. Henderson,et al.  A comparison of reversible jump MCMC algorithms for DNA sequence segmentation using hidden Markov models , 2001 .

[45]  Leopold Sögner,et al.  Selection of the number of states by birth-death processes , 2000 .

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

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

[48]  A. Gelman,et al.  Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .

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

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

[51]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[52]  M. Clyde,et al.  Model Uncertainty , 2003 .

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

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

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

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

[57]  Ioannis Ntzoufras,et al.  Bayesian hypothesis testing for the distribution of insurance claim counts using the Gibbs sampler , 2005, J. Comput. Methods Sci. Eng..

[58]  Refik Soyer,et al.  Bayesian Methods for Nonlinear Classification and Regression , 2004, Technometrics.

[59]  G. Casella,et al.  Objective Bayesian Variable Selection , 2006 .

[60]  J. Rosenthal,et al.  On adaptive Markov chain Monte Carlo algorithms , 2005 .

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

[62]  J. Berger,et al.  Expected‐posterior prior distributions for model selection , 2002 .

[63]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

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

[65]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .

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

[67]  C. Andrieu,et al.  On the ergodicity properties of some adaptive MCMC algorithms , 2006, math/0610317.

[68]  M. Suchard,et al.  Bayesian selection of continuous-time Markov chain evolutionary models. , 2001, Molecular biology and evolution.

[69]  Birth-death MCMC methods for mixtures with unknown number of components , 2002 .

[70]  P. Bühlmann,et al.  Variable Length Markov Chains: Methodology, Computing, and Software , 2004 .

[71]  Chang-Jin Kim,et al.  A Bayesian Approach to Testing for Markov Switching in Univariate and Dynamic Factor Models , 2001 .

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

[73]  Fernando A. Quintana,et al.  Nonparametric Bayesian data analysis , 2004 .

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

[75]  Bradley P. Carlin,et al.  Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .

[76]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

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

[78]  R. Tweedie,et al.  Exponential Convergence of Langevin Diiusions and Their Discrete Approximations , 1997 .

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

[80]  David Bruce Wilson,et al.  Exact sampling with coupled Markov chains and applications to statistical mechanics , 1996, Random Struct. Algorithms.

[81]  Anatoly Zhigljavsky,et al.  Self-regenerative Markov chain Monte Carlo with adaptation , 2003 .

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

[83]  Munehisa Kasuya,et al.  Model Uncertainty of Real Exchange Rate Forecast over Mid-term Horizons , 2001 .

[84]  Christopher H. Jackson,et al.  Models for longitudinal data with censored changepoints , 2004 .

[85]  I. Ntzoufras Gibbs Variable Selection using BUGS , 2002 .

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

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

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

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

[90]  A.V. Kovalyov An O , 1995, Proceedings of Tenth International Symposium on Intelligent Control.

[91]  S. Chib,et al.  Marginal Likelihood From the Metropolis–Hastings Output , 2001 .

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

[93]  Petros Dellaportas,et al.  Bayesian Analysis of Extreme Values by Mixture Modeling , 2003 .

[94]  Luca Tardella,et al.  A geometric approach to transdimensional markov chain monte carlo , 2003 .

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