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Empirical supremum rejection sampling

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  • Brian S. Caffo

Abstract

Rejection sampling thins out samples from a candidate density from which it is easy to simulate, to obtain samples from a more awkward target density. A prerequisite is knowledge of the finite supremum of the ratio of the target and candidate densities. This severely restricts application of the method because it can be difficult to calculate the supremum. We use theoretical argument and numerical work to show that a practically perfect sample may be obtained by replacing the exact supremum with the maximum obtained from simulated candidates. We also provide diagnostics for failure of the method caused by a bad choice of candidate distribution. The implication is that essentially no theoretical work is required to apply rejection sampling in many practical cases. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Brian S. Caffo, 2002. "Empirical supremum rejection sampling," Biometrika, Biometrika Trust, vol. 89(4), pages 745-754, December.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:4:p:745-754
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    Cited by:

    1. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    2. Buzbas, Erkan Ozge & Joyce, Paul & Rosenberg, Noah A., 2011. "Inference on the strength of balancing selection for epistatically interacting loci," Theoretical Population Biology, Elsevier, vol. 79(3), pages 102-113.

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