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Subsampling MCMC - an Introduction for the Survey Statistician

Author

Listed:
  • Matias Quiroz

    (University of New South Wales)

  • Mattias Villani

    (Stockholm University
    Linköping University)

  • Robert Kohn

    (University of New South Wales)

  • Minh-Ngoc Tran

    (University of Sydney)

  • Khue-Dung Dang

    (University of New South Wales)

Abstract

The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets. Data subsampling has recently been suggested as a way to make MCMC methods scalable on massively large data, utilizing efficient sampling schemes and estimators from the survey sampling literature. These developments tend to be unknown by many survey statisticians who traditionally work with non-Bayesian methods, and rarely use MCMC. Our article explains the idea of data subsampling in MCMC by reviewing one strand of work, Subsampling MCMC, a so called Pseudo-Marginal MCMC approach to speeding up MCMC through data subsampling. The review is written for a survey statistician without previous knowledge of MCMC methods since our aim is to motivate survey sampling experts to contribute to the growing Subsampling MCMC literature.

Suggested Citation

  • Matias Quiroz & Mattias Villani & Robert Kohn & Minh-Ngoc Tran & Khue-Dung Dang, 2018. "Subsampling MCMC - an Introduction for the Survey Statistician," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 33-69, December.
    • Handle: RePEc:spr:sankha:v:80:y:2018:i:1:d:10.1007_s13171-018-0153-7
    DOI: 10.1007/s13171-018-0153-7
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    References listed on IDEAS

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    Cited by:

    1. Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc & Villani, Mattias, 2019. "Hamiltonian Monte Carlo with Energy Conserving Subsampling," Working Paper Series 372, Sveriges Riksbank (Central Bank of Sweden).

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