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2016•Unpublished venueRequires access

A new combined sampling method based on variance minimization strategy

Xiaotian Xie, Wei Li, Lingyun Lu, Ming Yang

Open publisher page 1 citations

Abstract

Due to the apparent drawbacks of Crude Monte Carlo method (CMC)-including requiring a large number of samples, comparatively lower sampling efficiency, an improved sampling technique based on Variance Minimization (VM) strategy is proposed. The VM strategy is a versatile and widely-applied adaptive importance sampling technique. In this paper, the VM strategy is applied to find an importance probability density function (IPDF) before sampling. And then the obtained IPDF is sampled by Latin Hypercube Sampling (LHS). Besides, the variance of test function can be further reduced through Antithetic Random Variables (ARV) technique. The simulation results of two examples show that this new combined sampling method based on VM strategy (CSMVMS) can effectively reduce sample size and enhance efficiency under certain level of precision.

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What this paper is about

Due to the apparent drawbacks of Crude Monte Carlo method (CMC)-including requiring a large number of samples, comparatively lower sampling efficiency, an improved sampling technique based on Variance Minimization (VM) strategy is proposed. The VM strategy is a versatile and widely-applied adaptive importance sampling technique. In this paper, the VM strategy is applied to find an importance probability density function (IPDF) before sampling. And then the obtained IPDF is sampled by Latin Hypercube Sampling (LHS). Besides, the variance of test function can be further reduced through Antithetic Random Variables (ARV) technique. The simulation results of two examples show that this new combined sampling method based on VM strategy (CSMVMS) can effectively reduce sample size and enhance efficiency under certain level of precision.

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Available abstract

Due to the apparent drawbacks of Crude Monte Carlo method (CMC)-including requiring a large number of samples, comparatively lower sampling efficiency, an improved sampling technique based on Variance Minimization (VM) strategy is proposed. The VM strategy is a versatile and widely-applied adaptive importance sampling technique. In this paper, the VM strategy is applied to find an importance probability density function (IPDF) before sampling. And then the obtained IPDF is sampled by Latin Hypercube Sampling (LHS). Besides, the variance of test function can be further reduced through Antithetic Random Variables (ARV) technique. The simulation results of two examples show that this new combined sampling method based on VM strategy (CSMVMS) can effectively reduce sample size and enhance efficiency under certain level of precision.

Key concepts: Latin hypercube sampling, Sampling (signal processing), Slice sampling, Variance (accounting), Rejection sampling, Minification, Computer science, Monte Carlo method

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