importance sampling

'importance sampling' can also refer to...

importance sampling

importance sampling

Importance sampling for smoothing

Optimised importance sampling quantile estimation

Importance Sampling Via the Estimated Sampler

Sampling Saproxylic Coleoptera: Scale Issues and the Importance of Behavior

Conservative hypothesis tests and confidence intervals using importance sampling

Efficient importance sampling for events of moderate deviations with applications

On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation

Importance of Sampling Along a Vertical Gradient to Compare the Insect Fauna in Managed Forests

An importance sampling algorithm for exact conditional tests in log-linear models

CIS: compound importance sampling method for protein–DNA binding site p-value estimation

Density guided importance sampling: application to a reduced model of protein folding

Importance of Numeracy as a Risk Factor for Elder Financial Exploitation in a Community Sample

Analysis of Host–Parasite Incongruence in Papillomavirus Evolution Using Importance Sampling

Importance of Pre-Analytical Factors Contributing to Measurement Uncertainty, When Determining Sulfadoxine and Sulfamethoxazole from Capillary Blood Dried on Sampling Paper

DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks

Molecular systematics of cytochrome oxidase I and 16S from Neochlamisus leaf beetles and the importance of sampling.

Modeling the Importance of Sample Size in Relation to Error in MHC-Based Mate-Choice Studies on Natural Populations


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A method of reducing the variance of an estimate obtained via simulation. Suppose, for example, that we wish to estimate the quantity I, given by , where g is a given function. An obvious method is to generate uniform pseudo-random numbers u1, u2,…, un, in the interval (0, 1). Writing u1, u2,…, un for the corresponding random variables, the estimator, I1, is . Importance sampling makes a more representative choice of values: suppose that f is a probability density function that resembles g in its general shape, and let F be the corresponding distribution function. Instead of working with u1, u2,…, un, we work with ν1, ν2,…, νn, where F(νj)=uj. The resulting estimator, I2, given by , will have a smaller variance than I1.

Subjects: Probability and Statistics.

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