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Recalibration: A post-processing method for approximate Bayesian computation

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  • Rodrigues, G.S.
  • Prangle, D.
  • Sisson, S.A.

Abstract

A new recalibration post-processing method is presented to improve the quality of the posterior approximation when using Approximate Bayesian Computation (ABC) algorithms. Recalibration may be used in conjunction with existing post-processing methods, such as regression-adjustments. In addition, recalibration extends and strengthens the links between ABC and indirect inference algorithms, allowing more extensive use of misspecified auxiliary models in the ABC context. The method is illustrated using simulated examples to demonstrate the effects of recalibration under various conditions, and through an application to an analysis of stereological extremes both with and without the use of auxiliary models. Code to implement recalibration post-processing is available in the R package, abctools.

Suggested Citation

  • Rodrigues, G.S. & Prangle, D. & Sisson, S.A., 2018. "Recalibration: A post-processing method for approximate Bayesian computation," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 53-66.
    • Handle: RePEc:eee:csdana:v:126:y:2018:i:c:p:53-66
    DOI: 10.1016/j.csda.2018.04.004
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    References listed on IDEAS

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    Cited by:

    1. George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.

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