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.
The work of this author was supported by Finnish Academy under contract no. 3385
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Pennanen, T., Koivu, M. (2006). An Adaptive Importance Sampling Technique. In: Niederreiter, H., Talay, D. (eds) Monte Carlo and Quasi-Monte Carlo Methods 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31186-6_27
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DOI: https://doi.org/10.1007/3-540-31186-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25541-3
Online ISBN: 978-3-540-31186-7
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