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Automatically tuned general-purpose MCMC via new adaptive diagnostics

Author

Listed:
  • Jinyoung Yang

    (University of Toronto)

  • Jeffrey S. Rosenthal

    (University of Toronto)

Abstract

Adaptive Markov Chain Monte Carlo (MCMC) algorithms attempt to ‘learn’ from the results of past iterations so the Markov chain can converge quicker. Unfortunately, adaptive MCMC algorithms are no longer Markovian, so their convergence is difficult to guarantee. In this paper, we develop new diagnostics to determine whether the adaption is still improving the convergence. We present an algorithm which automatically stops adapting once it determines further adaption will not increase the convergence speed. Our algorithm allows the computer to tune a ‘good’ Markov chain through multiple phases of adaption, and then run conventional non-adaptive MCMC. In this way, the efficiency gains of adaptive MCMC can be obtained while still ensuring convergence to the target distribution.

Suggested Citation

  • Jinyoung Yang & Jeffrey S. Rosenthal, 2017. "Automatically tuned general-purpose MCMC via new adaptive diagnostics," Computational Statistics, Springer, vol. 32(1), pages 315-348, March.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:1:d:10.1007_s00180-016-0682-2
    DOI: 10.1007/s00180-016-0682-2
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    References listed on IDEAS

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    1. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).
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