Inverse transform sampling: Difference between revisions

Revision as of 15:28, 6 May 2020

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== Reduction of the number of inversions ==

In order to obtain a large number of samples, one needs to perform the same number of inversions of the distribution.

One possible way to reduce the number of inversions while obtaining a large number of samples is the application of the so-called Stochastic Collocation Monte Carlo sampler (SCMC sampler) within a [[polynomial chaos]] expansion framework. This allows us to generate any number of Monte Carlo samples with only a few inversions of the original distribution with independent samples of a variable for which the inversions are analytically available, for example the standard normal variable.<ref>L.A. Grzelak, J.A.S. Witteveen, M. Suarez, and C.W. Oosterlee. The stochastic collocation Monte Carlo sampler: Highly efficient sampling from “expensive” distributions. httphttps://papers.ssrn.com/sol3/papers.cfm?abstract_idabstract=2529691</ref>

== See also ==