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A weighted discrepancy bound of quasi-Monte Carlo importance sampling

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  • Dick, Josef
  • Rudolf, Daniel
  • Zhu, Houying

Abstract

Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method.

Suggested Citation

  • Dick, Josef & Rudolf, Daniel & Zhu, Houying, 2019. "A weighted discrepancy bound of quasi-Monte Carlo importance sampling," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 100-106.
    • Handle: RePEc:eee:stapro:v:149:y:2019:i:c:p:100-106
    DOI: 10.1016/j.spl.2019.01.014
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    References listed on IDEAS

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    1. Vandewoestyne, Bart & Cools, Ronald, 2010. "On the convergence of quasi-random sampling/importance resampling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(3), pages 490-505.
    2. Mathieu Gerber & Nicolas Chopin, 2015. "Sequential quasi Monte Carlo," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 509-579, June.
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