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

Tumor phylogeny inference using tree-constrained importance sampling

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

The Critical Importance of Sampling Fraction to Inferences of Mycobacterium tuberculosis Transmission

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


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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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