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Efficient Monte Carlo simulation via the generalized splitting method

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Abstract

We describe a new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multidimensional densities. The algorithm is inspired by the classical splitting method and can be applied to general static simulation models. We provide examples from rare-event probability estimation, counting, and sampling, demonstrating that the proposed method can outperform existing Markov chain sampling methods in terms of convergence speed and accuracy.

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Authors and Affiliations

  1. Department of Computer Science and Operations Research, University of Montreal, Quebec, 6128, Canada

    Zdravko I. Botev

  2. School of Mathematic and Physics, The University of Queensland, Brisbane, 4072, Australia

    Dirk P. Kroese

Authors
  1. Zdravko I. Botev
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  2. Dirk P. Kroese
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Correspondence to Zdravko I. Botev.

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Botev, Z.I., Kroese, D.P. Efficient Monte Carlo simulation via the generalized splitting method. Stat Comput 22, 1–16 (2012). https://doi.org/10.1007/s11222-010-9201-4

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  • DOI: https://doi.org/10.1007/s11222-010-9201-4

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