Descriptive Sampling: A Better Approach to Monte Carlo Simulation

Saliby, Eduardo

doi:10.1038/sj/jors/0411205

Cited by 13 publications

(16 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is based on a deterministic selection of the input sample values and their random permutations. The usefulness of DS was confirmed in several comparisons conducted by Saliby [14][15][16]. The estimate obtained by DS is unbiased and has a lower variance than that obtained by the classical method of CMCS [17,18].…”

Section: Introductionmentioning

confidence: 55%

“…Once they vary, simulation estimates will also vary. Therefore, both features can be ascertained as the two sources of variability of simulation estimates [15,16,25].…”

Section: Concept Of Descriptive Samplingmentioning

confidence: 99%

“…Unlike in simple random sampling, the set of values is the same for all replicated runs, r n , in a simulation. This random shuffling process is easily accomplished by sampling the descriptive set of values without replacement [15].…”

Section: Concept Of Descriptive Samplingmentioning

confidence: 99%

See 2 more Smart Citations

Choosing a suitable sample size in descriptive sampling

2010

View full text Add to dashboard Cite

Descriptive sampling (DS) is an alternative to crude Monte Carlo sampling (CMCS) in finding solutions to structural reliability problems. It is known to be an effective sampling method in approximating the distribution of a random variable because it uses the deterministic selection of sample values and their random permutation,. However, because this method is difficult to apply to complex simulations, the sample size is occasionally determined without thorough consideration. Input sample variability may cause the sample size to change between runs, leading to poor simulation results. This paper proposes a numerical method for choosing a suitable sample size for use in DS. Using this method, one can estimate a more accurate probability of failure in a reliability problem while running a minimal number of simulations. The method is then applied to several examples and compared with CMCS and conventional DS to validate its usefulness and efficiency.

show abstract

Section: Introductionmentioning

confidence: 55%

“…Once they vary, simulation estimates will also vary. Therefore, both features can be ascertained as the two sources of variability of simulation estimates [15,16,25].…”

Section: Concept Of Descriptive Samplingmentioning

confidence: 99%

See 1 more Smart Citation

Choosing a suitable sample size in descriptive sampling

2010

View full text Add to dashboard Cite

show abstract

“…More details can be found in literature [39,40]. This sampling technique is one of the variance reduction techniques, allowing to reduce the variance of the statistical estimates derived from the MCS data.…”

Section: ( ) P ⋅mentioning

confidence: 99%

“…A simple use of random sampling usually requires much more than a desirable number of sample points and is even impractical for complex and time-consuming analyses. To achieve high accuracy of the statistical description of the system behavior, a descriptive sampling method [39,40] is used in this study.…”

Section: Monte Carlo Simulation and Descriptive Samplingmentioning

confidence: 99%

Crashworthiness design of foam-filled bitubal structures with uncertainty

Fang

Gao

Sun

et al. 2014

International Journal of Non-Linear Mechanics

View full text Add to dashboard Cite

Sampling Techniques

Diwekar

Ulas

2007

Kirk-Othmer Encyclopedia of Chemical Technology

View full text Add to dashboard Cite

Sampling is a statistical procedure that involves the selection of a finite number of individuals to represent and infer some knowledge about a population of concern. Sampling techniques are used in a wide range of science and engineering applications; they are of basic importance in computational statistics, in the implementation of probabilistic algorithms, and in related problems of statistical computing that have a stochastic ingredient (eg, financial modeling, artificial intelligence, computational chemistry, risk and uncertainty analysis, and design of experiments). This article is devoted to the role of sampling in process systems engineering.

show abstract

Descriptive Sampling: A Better Approach to Monte Carlo Simulation

Cited by 13 publications

References 0 publications

Choosing a suitable sample size in descriptive sampling

Choosing a suitable sample size in descriptive sampling

Crashworthiness design of foam-filled bitubal structures with uncertainty

Sampling Techniques

Contact Info

Product

Resources

About