In modeling particle transport through a medium, the path of a
particle behaves as a transient Markov chain. We are interested in
characteristics of the particle's movement conditional on its starting state,
which take the form of a "score" accumulated with each transition. Importance
sampling is an essential variance reduction technique in this setting, and we
provide an adaptive (iteratively updated) importance sampling algorithm that
converges exponentially to the solution. Examples illustrating this phenomenon
are provided.
Publié le : 1999-05-14
Classification:
Adaptive procedures,
exponential convergence,
Monte Carlo,
particle transport,
zero-variance solution,
65C05
@article{1029962748,
author = {Kollman, Craig and Baggerly, Keith and Cox, Dennis and Picard, Rick},
title = {Adaptive importance sampling on discrete Markov chains},
journal = {Ann. Appl. Probab.},
volume = {9},
number = {1},
year = {1999},
pages = { 391-412},
language = {en},
url = {http://dml.mathdoc.fr/item/1029962748}
}
Kollman, Craig; Baggerly, Keith; Cox, Dennis; Picard, Rick. Adaptive importance sampling on discrete Markov chains. Ann. Appl. Probab., Tome 9 (1999) no. 1, pp. 391-412. http://gdmltest.u-ga.fr/item/1029962748/