Using Supervised Learning to Improve Monte Carlo Integral Estimation

Details Using Supervised Learning to Improve Monte Carlo Integral Estimation Using Supervised Learning to Improve Monte Carlo Integral Estimation

2011-08-24

arxiv.org/abs/1108.4879v1

13th AIAA Non-Deterministic Approaches Conference, Denver, CO, April 2011, AIAA Paper 2011-1843

Statistics

Machine Learning

18 pages, 10 figures, originally published by AIAA at the 13th Non-Deterministic Approaches Conference

Scientific paper

Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e.g. performance measures) becomes important. However, MC techniques often suffer from high variance and slow convergence as the number of samples increases. In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated integral estimate. StackMC is based on the supervised learning techniques of fitting functions and cross validation. It should reduce the variance of any type of Monte Carlo integral estimate (simple sampling, importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We report on an extensive set of experiments confirming that the StackMC estimate of an integral is more accurate than both the associated unprocessed Monte Carlo estimate and an estimate based on a functional fit to the MC samples. These experiments run over a wide variety of integration spaces, numbers of sample points, dimensions, and fitting functions. In particular, we apply StackMC in estimating the expected value of the fuel burn metric of future commercial aircraft and in estimating sonic boom loudness measures. We compare the efficiency of StackMC with that of more standard methods and show that for negligible additional computational cost significant increases in accuracy are gained.

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