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An Adaptive Importance Sampling Technique

In: Monte Carlo and Quasi-Monte Carlo Methods 2004

Author

Listed:
  • Teemu Pennanen

    (Helsinki School of Economics, Department of Management Science)

  • Matti Koivu

    (Helsinki School of Economics, Department of Management Science)

Abstract

Summary This paper proposes a new adaptive importance sampling (AIS) technique for approximate evaluation of multidimensional integrals. Whereas known AIS algorithms try to find a sampling density that is approximately proportional to the integrand, our algorithm aims directly at the minimization of the variance of the sample average estimate. Our algorithm uses piecewise constant sampling densities, which makes it also reminiscent of stratified sampling. The algorithm was implemented in C-programming language and compared with VEGAS and MISER.

Suggested Citation

  • Teemu Pennanen & Matti Koivu, 2006. "An Adaptive Importance Sampling Technique," Springer Books, in: Harald Niederreiter & Denis Talay (ed.), Monte Carlo and Quasi-Monte Carlo Methods 2004, pages 443-455, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-31186-7_27
    DOI: 10.1007/3-540-31186-6_27
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