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Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method


  • C. P. Robert
  • T. Rydén
  • D. M. Titterington


No abstract is available for this item.

Suggested Citation

  • C. P. Robert & T. Rydén & D. M. Titterington, 2000. "Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 57-75.
  • Handle: RePEc:bla:jorssb:v:62:y:2000:i:1:p:57-75

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    Cited by:

    1. Hartman, Brian M. & Heaton, Matthew J., 2011. "Accounting for regime and parameter uncertainty in regime-switching models," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 429-437.
    2. Deschamps, Philippe J., 2006. "A flexible prior distribution for Markov switching autoregressions with Student-t errors," Journal of Econometrics, Elsevier, vol. 133(1), pages 153-190, July.
    3. Salima El Kolei, 2013. "Parametric estimation of hidden stochastic model by contrast minimization and deconvolution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1031-1081, November.
    4. John Liechty & Rik Pieters & Michel Wedel, 2003. "Global and local covert visual attention: Evidence from a bayesian hidden markov model," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 519-541, December.
    5. Ho, Remus K.W. & Hu, Inchi, 2008. "Flexible modelling of random effects in linear mixed models--A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1347-1361, January.
    6. repec:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0283-8 is not listed on IDEAS
    7. McGrory, C.A. & Titterington, D.M., 2007. "Variational approximations in Bayesian model selection for finite mixture distributions," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5352-5367, July.
    8. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April.
    9. Nial Friel & Håvard Rue, 2007. "Recursive computing and simulation-free inference for general factorizable models," Biometrika, Biometrika Trust, vol. 94(3), pages 661-672.
    10. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    11. McGrory, C.A. & Pettitt, A.N. & Faddy, M.J., 2009. "A fully Bayesian approach to inference for Coxian phase-type distributions with covariate dependent mean," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4311-4321, October.
    12. Chih-chiang Yang, 2007. "Confirmatory and Structural Categorical Latent Variables Models," Quality & Quantity: International Journal of Methodology, Springer, vol. 41(6), pages 831-849, December.
    13. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
    14. repec:bla:jorssc:v:66:y:2017:i:4:p:691-715 is not listed on IDEAS
    15. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
    16. Francesco Bartolucci, 2002. "A recursive algorithm for Markov random fields," Biometrika, Biometrika Trust, vol. 89(3), pages 724-730, August.

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