Monte Carlo simulation for moment-independent sensitivity analysis
The moment-independent sensitivity analysis (SA) is one of the most popular SA techniques. It aims at measuring the contribution of input variable(s) to the probability density function (PDF) of model output. However, compared with the variance-based one, robust and efficient methods are less availa...
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Veröffentlicht in: | Reliability engineering & system safety 2013-02, Vol.110, p.60-67 |
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description | The moment-independent sensitivity analysis (SA) is one of the most popular SA techniques. It aims at measuring the contribution of input variable(s) to the probability density function (PDF) of model output. However, compared with the variance-based one, robust and efficient methods are less available for computing the moment-independent SA indices (also called delta indices). In this paper, the Monte Carlo simulation (MCS) methods for moment-independent SA are investigated. A double-loop MCS method, which has the advantages of high accuracy and easy programming, is firstly developed. Then, to reduce the computational cost, a single-loop MCS method is proposed. The later method has several advantages. First, only a set of samples is needed for computing all the indices, thus it can overcome the problem of “curse of dimensionality”. Second, it is suitable for problems with dependent inputs. Third, it is purely based on model output evaluation and density estimation, thus can be used for model with high order (>2) interactions. At last, several numerical examples are introduced to demonstrate the advantages of the proposed methods. |
doi_str_mv | 10.1016/j.ress.2012.09.005 |
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It aims at measuring the contribution of input variable(s) to the probability density function (PDF) of model output. However, compared with the variance-based one, robust and efficient methods are less available for computing the moment-independent SA indices (also called delta indices). In this paper, the Monte Carlo simulation (MCS) methods for moment-independent SA are investigated. A double-loop MCS method, which has the advantages of high accuracy and easy programming, is firstly developed. Then, to reduce the computational cost, a single-loop MCS method is proposed. The later method has several advantages. First, only a set of samples is needed for computing all the indices, thus it can overcome the problem of “curse of dimensionality”. Second, it is suitable for problems with dependent inputs. Third, it is purely based on model output evaluation and density estimation, thus can be used for model with high order (>2) interactions. 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subjects | Computation Computational efficiency Computer simulation Delta indices Density Exact sciences and technology Kernel density estimation Mathematical models Mathematics Moment-independent sensitivity analysis Monte Carlo methods Monte Carlo simulation Nonparametric inference Probability and statistics Probability density functions Sciences and techniques of general use Sensitivity analysis Statistics |
title | Monte Carlo simulation for moment-independent sensitivity analysis |
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