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Comparison of methodologies to assess the convergence of Markov chain Monte Carlo methods

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  • El Adlouni, Salaheddine
  • Favre, Anne-Catherine
  • Bobee, Bernard

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  • El Adlouni, Salaheddine & Favre, Anne-Catherine & Bobee, Bernard, 2006. "Comparison of methodologies to assess the convergence of Markov chain Monte Carlo methods," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2685-2701, June.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:10:p:2685-2701
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    References listed on IDEAS

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    1. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
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    1. Boulange, Julien & Watanabe, Hirozumi & Akai, Shinpei, 2017. "A Markov Chain Monte Carlo technique for parameter estimation and inference in pesticide fate and transport modeling," Ecological Modelling, Elsevier, vol. 360(C), pages 270-278.
    2. Ousmane Seidou & Andrea Ramsay & Ioan Nistor, 2012. "Climate change impacts on extreme floods I: combining imperfect deterministic simulations and non-stationary frequency analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 647-659, March.
    3. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    4. Richard Andrew Iles, 2013. "Demand for primary healthcare in rural north India," 2013 Papers pil50, Job Market Papers.
    5. Alexander Garcia-Aristizabal & Edoardo Bucchignani & Elisa Palazzi & Donatella D’Onofrio & Paolo Gasparini & Warner Marzocchi, 2015. "Analysis of non-stationary climate-related extreme events considering climate change scenarios: an application for multi-hazard assessment in the Dar es Salaam region, Tanzania," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 289-320, January.
    6. Jia, Xiang & Wang, Dong & Jiang, Ping & Guo, Bo, 2016. "Inference on the reliability of Weibull distribution with multiply Type-I censored data," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 171-181.
    7. Gonzalez, M. & Martin, J. & Martinez, R. & Mota, M., 2008. "Non-parametric Bayesian estimation for multitype branching processes through simulation-based methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1281-1291, January.
    8. Calderhead, Ben & Girolami, Mark, 2009. "Estimating Bayes factors via thermodynamic integration and population MCMC," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4028-4045, October.
    9. Stefan Habenschuss & Zeno Jonke & Wolfgang Maass, 2013. "Stochastic Computations in Cortical Microcircuit Models," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-28, November.

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