Annealed importance sampling reversible jump MCMC algorithms

Journal of Computational and Graphical Statistics

Volumn 22, Issue 3, 2013, Pages 623-648

  Karagiannis, Georgios ^a   Andrieu, Christophe ^b  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Bayesian model selection determination; Gaussian mixture models; Poisson change point problem; Pseudo marginal MCMC

Indexed keywords

EID: 84890169240     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1080/10618600.2013.805651     Document Type: Article

References (17)

1. Al-Awadhi, F., Hurn, M., and Jennison, C. (2004), "Improving the Acceptance Rate of Reversible Jump MCMC Proposals, " Statistics & Probability Letters, 69, 189-198. [626, 632].
2. Andrieu, C., Doucet, A., and Holenstein, R. (2010), "Particle Markov Chain Monte Carlo Methods, " Journal of the Royal Statistical Society, Series B, 72, 269-342. [624].
3. Andrieu, C., and Roberts, G. (2009), "The Pseudo-Marginal Approach for Efficient Monte Carlo Computations, " The Annals of Statistics, 37, 697-725. [624, 634].
4. Andrieu, C., and Thoms, J. (2008), "A Tutorial on Adaptive MCMC, " Statistics and Computing, 18, 343-373. [647].
5. Andrieu, C., and Vihola, M. (2012), "Convergence Properties of Pseudo-Marginal Markov Chain Monte Carlo Algorithms, " ArXiv e-prints. [626].
6. Brooks, S., Giudici, P., and Roberts, G. (2003), "Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions, " Journal of the Royal Statistical Society, Series B, 65, 3-55. [626].
7. Gelman, A., and Meng, X. (1998), "Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling, " Statistical Science, 163-185. [631].
8. Green, P. (1995), "Reversible Jump Markov Chain MonteCarlo Computation and Bayesian ModelDetermination, " Biometrika, 82, 711-732. [623, 627, 633, 637, 638, 647].
9. Hendrix, D., and Jarzynski, C. (2001), "A 'Fast Growth' Method of Computing Free Energy Differences, " The Journal of Chemical Physics, 114, 5974-5981. [647].
10. Jarzynski, C. (1997a), "Equilibrium Free-Energy Differences From Nonequilibrium Measurements: A Master-Equation Approach, " Physical Review E, 56, 5018-5035. [624, 626, 632, 647].
11. Jarzynski, C.(1997b), "Nonequilibrium Equality for Free EnergyDifferences, " Physical ReviewLetters, 78, 2690-2693. [624, 626, 647].
12. Karagiannis, G. (2011), Annealed Importance Sampling Reversible JumpMarkov chainMonte Carlo, unpublished Ph. D. dissertation, University of Bristol, Department of Mathematics. [633].
13. Neal, R. (1996), "Sampling FromMultimodal Distributions Using Tempered Transitions, " Statistics and Computing, 6, 353-366. [624, 632].
14. Neal, R. (2001), "Annealed Importance Sampling, " Statistics and Computing, 11, 125-139. [624, 626, 632].
15. Neal, R. (2004), "Taking BiggerMetropolis Steps byDragging FastVariables, " Technical Report 0411, Department of Statistics, University of Toronto. [624, 629, 632, 647].
16. Richardson, S., and Green, P. (1997), "On Bayesian Analysis of Mixtures With an Unknown Number of Components" (with discussion), Journal of the Royal Statistical Society, Series B, 59, 731-792. [633, 642, 645].
17. Roberts, G., and Rosenthal, J. (1998), "Optimal Scaling of Discrete Approximations to Langevin Diffusions, " Journal of the Royal Statistical Society, Series B, 60, 255-268. [634, 637].