Monte Carlo Methods

This chapter deals with basic concepts and definitions concerning Monte Carlo sampling techniques. Rejection sampling and importance sampling are first introduced. Markov chains and some of their basic properties are discussed. Then the Metropolis-Hastings and Gibbs algorithms are presented. At the end of the chapter, a case study concerning change point detection is considered.

[1]  S. Walker Invited comment on the paper "Slice Sampling" by Radford Neal , 2003 .

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

[3]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

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

[5]  G. Parisi,et al.  Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.

[6]  W. Gilks,et al.  Adaptive Rejection Sampling for Gibbs Sampling , 1992 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  Donald Ervin Knuth,et al.  The Art of Computer Programming , 1968 .

[9]  Uffe Kjærulff,et al.  Blocking Gibbs sampling in very large probabilistic expert systems , 1995, Int. J. Hum. Comput. Stud..

[10]  Radford M. Neal Slice Sampling , 2003, The Annals of Statistics.

[11]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[12]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[13]  Elizabeth L. Wilmer,et al.  Markov Chains and Mixing Times , 2008 .

[14]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  L. Tierney Markov Chains for Exploring Posterior Distributions , 1994 .

[16]  A. Taylan Cemgil,et al.  Chapter 19 - A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering , 2014 .

[17]  Elchanan Mossel,et al.  The Computational Complexity of Estimating Convergence Time , 2010, ArXiv.

[18]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[19]  R. Carroll,et al.  Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples , 2010 .

[20]  Alistair Sinclair,et al.  Algorithms for Random Generation and Counting: A Markov Chain Approach , 1993, Progress in Theoretical Computer Science.

[21]  S. K. Park,et al.  Random number generators: good ones are hard to find , 1988, CACM.

[22]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[23]  J. Hartigan,et al.  A Bayesian Analysis for Change Point Problems , 1993 .

[24]  Brian D. Ripley,et al.  Stochastic Simulation , 2005 .

[25]  L. Devroye Non-Uniform Random Variate Generation , 1986 .

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

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

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

[29]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[30]  Walter R. Gilks,et al.  Adaptive Direction Sampling , 1994 .

[31]  J. Møller,et al.  An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants , 2006 .

[32]  D. Higdon Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications , 1998 .

[33]  Jun S. Liu,et al.  The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .

[34]  Robert Lund,et al.  A Review and Comparison of Changepoint Detection Techniques for Climate Data , 2007 .

[35]  T. Lai Sequential changepoint detection in quality control and dynamical systems , 1995 .