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A Review on the Exact Monte Carlo Simulation

In: Bayesian Inference on Complicated Data

Author

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  • Hongsheng Dai

Abstract

Perfect Monte Carlo sampling refers to sampling random realizations exactly from the target distributions (without any statistical error). Although many different methods have been developed and various applications have been implemented in the area of perfect Monte Carlo sampling, it is mostly referred by researchers to coupling from the past (CFTP) which can correct the statistical errors for the Monte Carlo samples generated by Markov chain Monte Carlo (MCMC) algorithms. This paper provides a brief review on the recent developments and applications in CFTP and other perfect Monte Carlo sampling methods.

Suggested Citation

  • Hongsheng Dai, 2020. "A Review on the Exact Monte Carlo Simulation," Chapters, in: Niansheng Tang (ed.), Bayesian Inference on Complicated Data, IntechOpen.
    • Handle: RePEc:ito:pchaps:199896
    DOI: 10.5772/intechopen.88619
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    More about this item

    Keywords

    coupling from the past; diffusion; Monte Carlo; perfect sampling;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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