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Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century

Citations

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Cited by:

  1. Guedon, Yann, 2007. "Exploring the state sequence space for hidden Markov and semi-Markov chains," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2379-2409, February.
  2. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
  3. Netzer, Oded & Lattin, James M. & Srinivasan, V. Seenu, 2007. "A Hidden Markov Model of Customer Relationship Dynamics," Research Papers 1904r, Stanford University, Graduate School of Business.
  4. R. Reeves, 2004. "Efficient recursions for general factorisable models," Biometrika, Biometrika Trust, vol. 91(3), pages 751-757, September.
  5. Gelman Andrew & Robert Christian P. & Rousseau Judith, 2013. "Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin," Statistics & Risk Modeling, De Gruyter, vol. 30(2), pages 105-120, June.
  6. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
  7. McCausland, William J., 2007. "Time reversibility of stationary regular finite-state Markov chains," Journal of Econometrics, Elsevier, vol. 136(1), pages 303-318, January.
  8. 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.
  9. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
  10. Chang-Jin Kim & Jaeho Kim, 2013. "Bayesian Inference in Regime-Switching ARMA Models with Absorbing States: The Dynamics of the Ex-Ante Real Interest Rate Under Structural Breaks," Discussion Paper Series 1306, Institute of Economic Research, Korea University.
  11. Rutger Jan Lange, 2020. "Bellman filtering for state-space models," Tinbergen Institute Discussion Papers 20-052/III, Tinbergen Institute, revised 19 May 2021.
  12. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
  13. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
  14. Ravishanker, Nalini & Liu, Zhaohui & Ray, Bonnie K., 2008. "NHPP models with Markov switching for software reliability," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3988-3999, April.
  15. Scott, Steven L., 2004. "A Bayesian paradigm for designing intrusion detection systems," Computational Statistics & Data Analysis, Elsevier, vol. 45(1), pages 69-83, February.
  16. Peter Ebbes & Rajdeep Grewal & Wayne DeSarbo, 2010. "Modeling strategic group dynamics: A hidden Markov approach," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 241-274, June.
  17. Koulis Theodoro & Muthukumarana Saman & Briercliffe Creagh Dyson, 2014. "A Bayesian stochastic model for batting performance evaluation in one-day cricket," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(1), pages 1-13, January.
  18. Murakami, Junko, 2009. "Bayesian posterior mean estimates for Poisson hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 941-955, February.
  19. Hugh Christensen & Simon Godsill & Richard E Turner, 2020. "Hidden Markov Models Applied To Intraday Momentum Trading With Side Information," Papers 2006.08307, arXiv.org.
  20. Nial Friel & Håvard Rue, 2007. "Recursive computing and simulation-free inference for general factorizable models," Biometrika, Biometrika Trust, vol. 94(3), pages 661-672.
  21. Bartolucci, Francesco, 2011. "An alternative to the Baum-Welch recursions for hidden Markov models," MPRA Paper 38778, University Library of Munich, Germany.
  22. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
  23. Congdon, Peter, 2006. "Bayesian model choice based on Monte Carlo estimates of posterior model probabilities," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 346-357, January.
  24. Kartik B. Athreya & Grey Gordon & John Bailey Jones & Urvi Neelakantan, 2021. "Incarceration, Earnings, and Race," Working Paper 21-11`, Federal Reserve Bank of Richmond.
  25. Penelope A. Smith & Peter M. Summers, 2004. "Identification and normalization in Markov switching models of \"business cycles\"," Research Working Paper RWP 04-09, Federal Reserve Bank of Kansas City.
  26. Hammer, Hugo & Tjelmeland, Håkon, 2011. "Approximate forward-backward algorithm for a switching linear Gaussian model," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 154-167, January.
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