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On some method for diagnosing convergence in MCMC setups via atoms and renewal sets

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Języki publikacji
EN
Abstrakty
EN
MCMC setups are among the best known methods for conducting computer simulations necessary in statistics, physics, biology, etc. However, to obtain appropriate solutions, additional convergence diagnosis must be applied for trajectory generated by Markov Chain. In the paper we present, the method for dealing with this problem, based on features of so called "secondary" chain (the chain with specially selected state space). The secondary chain is created from the initial chain by picking only some observations connected with atoms or renewal sets. The discussed method has some appealing properties, like high degree of diagnosis automation. Apart from theoretical lemmas, the example of application is also provided.
Rocznik
Strony
985--1008
Opis fizyczny
Bibliogr. 30 poz. , wykr.
Twórcy
autor
  • Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warszawa, Poland, mroman@ibspan.waw.pl
Bibliografia
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  • BOOS, D. and ZHANG, J. (2000) Monte Carlo Evaluation of Resampling -Based Hypothesis Tests. Journal of the American Statistical Association 95, No. 450.
  • BOOTH, J.G. and SARKAR, S. (1998) Monte Carlo Approximation of Bootstrap Variances. The American Statistician 52, 4.
  • BREMAUD, P. (1999) Markov Chains - Gibbs Fields, Monte Carlo Simulation, and Queues. Springer Verlag, New York.
  • BROOKS, S.P. and ROBERTS, G.O. (1998) Convergence assessment techniques for Markov chain Monte Carlo. Statistics and Computing 8, 319-335.
  • COX, D.R. and MILLER, H.D. (1965) The Theory of Stochastic Processes. Chapman and Hall, London.
  • DOUCET, A., GODSILL, S. and ANDRIEU, CH. (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10.
  • FISHMAN, G.S. (1996) Monte Carlo - Concepts, Algorithms and Applications. Springer Verlag, New York.
  • GELFAND, A.E., HILLS, S.E., RACINE-POON, A. and SMITH, A.F.M. (1990) Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling. Journal of the American Statistical Association 85, 412.
  • GEYER, C.J. (1992) Practical Markov chain Monte Carlo (with discussion). Statist. Sci. 7, 473-511.
  • GILKS, W.R., RICHARDSON, S. and SPIEGELHALTER, D. J. (1997) Markov Chain Monte Carlo in Practice. Chapman & Hall.
  • GUIHENNEUC-JOUYAUX, CH. and ROBERT, CH.P. (1998) Discretization of Continuous Markov Chains and Markov Chain Monte Carlo Convergence Assessment. Jour, of American Stat. Assoc. 93, 443.
  • IOSIFESCU, M. (1980) Finite Markov Processes and Their Applications. Wiley, New York.
  • KASS, R.E., CARLIN, B.P., GELMAN, A. and NEAL, R.M. (1998) Markov Chain Monte Carlo in Practice: A Roundtable Discussion The American Statistician 52, 2.
  • KlPNIS, C. and VARADHAN, S.R. (1986) Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions Comm. Math. Phys. 104, 1-19.
  • KORONACKI, J., LASOTA, S. and NIEMIRO, W. (2005) Positron emission tomography by Markov chain Monte Carlo with auxiliary variables. Pattern Recognition 38, 241-250.
  • LASOTA, S. and NIEMIRO, W. (2003) A version of the Swendsen-Wand algorithm for restoration of images degraded by Poisson noise. Pattern Recognition 36, 931 - 941.
  • LI, S., PEARL, D.K. and DOSS, H. (2000) Phylogenetic Tree Construction Using Markov Chain Monte Carlo. Journal of the American Statistical Association 95, 450
  • MEHTA, C.R., PATEL, N.R. and SENCHAUDHURI, P. (2000) Efficient Monte Carlo Methods for Conditional Logistic Regression. Journal of the American Statistical Association 95, 449
  • MENGERSEN, K.L., ROBERT, CH.P. and GUIHENNEUC-JOUYAUX, CH. (1999) MCMC Convergence Diagnostics: A Review. In: J.M. Bernardo, J.O. Ber-ger, A.P. Dawid, A.P.M. Smith, eds., Bayesian Statistics 6, Oxford University Press, 415-440.
  • METROPOLIS, N., ROSENBLUTH, A.W., ROSENBLUTH, M.N., TELLER, A.H. and TELLER, E. (1953) Equations of state calculations by fast computing machines. J. Chem. Phys. 21.
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  • MYKLAND, P., TIERNEY, L. and YU, B. (1995) Regeneration in Markov Chain Samplers. JASA 90, 233 - 241.
  • NUMMELIN, E. (1978) A splitting technique for Harris recurrent Markov Chains. Zeitschrift für Wahrscheinlichkeitstheorie und verwändte Gebiete 43, 309 -318.
  • RAFTERY, A.E. and LEWIS, S.M. (1999) How many iterations in the Gibbs Sampler? In: J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.P.M. Smith, eds., Bayesian Statistics 4- Oxford University Press, 763-773.
  • ROBERT, CH.P. (1995) Convergence Control Methods for Markov Chain Monte Carlo Algorithm. Statistical Science 10, 3.
  • ROBERT, CH.P. and CASELLA, G. (2004) Monte Carlo Statistical Methods, 2nd ed., Springer Verlag, New York.
  • ROMANIUK, M. (2003) Pricing the Risk-Transfer Financial Instruments via Monte Carlo Methods. Systems Analysis Modelling Simulation 43, 8. 1043 - 1064.
  • ROMANIUK, M. (2007) Application of renewal sets for convergence diagnosis of MCMC setups (in Polish). Ph.D. dissertation, Systems Research Institute, Polish Academy of Sciences.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0026-0010
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