Importance Sampling Based on the Kernel Density Estimator

DOI

10.2991/icetms.2013.386How to use a DOI?

Keywords

Kernel Density Estimator;Importance Sampling

Abstract

Importance Sampling is an unbiased sampling method used to sample random variables form different densities than originally defined. The importance sampling densities should be constructed to pick up ‘important’ random variables to improve the estimation of a interesting statistics. In this article, we present an importance sampling in which its density function is constructed from the kernel density estimators. This method can generate a sufficient number of samples, and then increase the accuracy of the probability estimate.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - XueGao Zhang
PY  - 2013/06
DA  - 2013/06
TI  - Importance Sampling Based on the Kernel Density Estimator
BT  - Proceedings of the 2013 Conference on Education Technology and Management Science (ICETMS 2013)
PB  - Atlantis Press
SP  - 1441
EP  - 1443
SN  - 1951-6851
UR  - https://doi.org/10.2991/icetms.2013.386
DO  - 10.2991/icetms.2013.386
ID  - Zhang2013/06
ER  -