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About 10,000 papers for “Variance reduction”

Variance Reduction

Rituparna Sen, Sourish Das · Indian statistical institute series · 2023

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Variance Reduction

Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev · Wiley series in probability and statistics · 2011

The estimation of performance measures in Monte Carlo simulation can be made more efficient by utilizing known information about the simulation model. The more that is known about the behavior of the system, the greater the amount of variance reduction that can be achieved. The main variance reduction techniques discussed in this chapter are antithetic random variables, control variables, conditional Monte Carlo, stratification, latin hypercube sampling, importance sampling, and quasi Monte Carlo. Each of the variance reduction methods is illustrated using the following estimation problem concerning a bridge network. The problem is sufficiently complicated to warrant Monte Carlo simulation, while easy enough to implement, so that the workings of each technique can be concisely illustrated. Controlled Vocabulary Terms Latin hypercube sampling; Monte Carlo methods; random variables; stratified sampling; variance reduction

Cited by 1

. Variance reduction

In Any Of, N In · 2007

.46> ,K , x iM in such a way that s *2 < M -1 var p g x ij () [] . If in addition the cost of generating the M -tuple is insignificantly greater than the cost of generating M independent variables from p x () , then I N M provides a computationally more efficient approximation of I than does I N . There are numerous variants on this technique. This section takes up four that account for most use of the method: antithetic variables, systematic sampling, conditional expectations, and control variables. The scope for combining these variance reduction techniques with the methods of Section 4 or Section 6 is enormous. Rather than list all the possibilities, the purpose here is to provide some

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Variance reduction

Mircpnrsltokts Hr, George S. Fishman · 1983

of the United States government., Thia document hs len approvidT] for public release and sale; its distribution is unhmited.

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Variance Reduction

In Any · 2007

elation among x i1 ,K , x iM in such a way that s *2 < M -1 var p g x ij ( ) [ ] . If in addition the cost of generating the M -tuple is insignificantly greater than the cost of generating M independent variables from p x ( ) , then I N M provides a computationally more efficient approximation of I than does I N . There are numerous variants on this technique. This section takes up four that account for most use of the method: antithetic variables, systematic sampling, conditional expectations, and control variables. The scope for combining these variance reduction techniques with the methods of Section 4 or Section 6 is enormous. Rather than list all the pos

Cited by 0

Variance reduction results.

Tun Wiltgen (18155550), Cuici Voon (18155553), Koen Van Leemput (815680) et al. · Figshare · 2024

<div><p>Background</p><p>Conventional brain magnetic resonance imaging (MRI) produces image intensities that have an arbitrary scale, hampering quantification. Intensity scaling aims to overcome this shortfall. As neurodegenerative and inflammatory disorders may affect all brain compartments, reference regions within the brain may be misleading. Here we summarize approaches for intensity scaling of conventional T1-weighted (w) and T2w brain MRI avoiding reference regions within the brain.</p><p>Methods</p><p>Literature was searched in the databases of Scopus, PubMed, and Web of Science. We included only studies that avoided reference regions within the brain for intensity scaling and provided validating evidence, which we divided into four categories: 1) comparative variance reduction, 2) comparative correlation with clinical parameters, 3) relation to quantitative imaging, or 4) relation to histology.</p><p>Results</p><p>Of the 3825 studies screened, 24 fulfilled the inclusion criteria. Three studies used scaled T1w images, 2 scaled T2w images, and 21 T1w/T2w-ratio calculation (with double counts). A robust reduction in variance was reported. Twenty studies investigated the relation of scaled intensities to different types of quantitative imaging. Statistically significant correlations with clinical or demographic data were reported in 8 studies. Four studies reporting the relation to histology gave no clear picture of the main signal driver of conventional T1w and T2w MRI sequences.</p><p>Conclusions</p><p>T1w/T2w-ratio calculation was applied most often. Variance reduction and correlations with other measures suggest a biologically meaningful signal harmonization. However, there are open methodological questions and uncertainty on its biological underpinning. Validation evidence on other scaling methods is even sparser.</p></div>

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Variance Reduction Methods

2014

This chapter contains sections titled: Antithetic Sampling Common Random Numbers Control Variates Conditional Monte Carlo Stratified Sampling Importance Sampling

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