We propose an extension to Markov Chain Monte Carlo methods for inferences in the imprecise probability framework. The algorithm is based on simultaneous sampling from all the Markov chains targeting the distributions in the credal set. The algorithm constructs a chain of random sets, which can be used for conservative estimation of lower and upper expected values of derived random variables. Tight bounds on the set of estimators arising from the set of admitted stochastic models can be obtained when the credal set is finite for general models. Conservative bounds can be obtained for some classes of models also when the credal set is uncountable. Computational complexity for uncountable credal sets is not bounded, and heuristic fixes need to be implemented.


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    Title :

    Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference


    Additional title:

    Space Technol.Proceedings



    Conference:

    International Conference on Uncertainty Quantification & Optimisation ; 2020 ; Brussels, Belgium November 17, 2020 - November 20, 2020



    Publication date :

    2021-07-16


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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