Improving the Convergence of Iterative Importance Sampling for Computing Upper and Lower Expectations

Thomas Fetz
Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 103:185-193, 2019.

Abstract

The aim of this paper is to present methods for improving the convergence of an iterative importance sampling algorithm for calculating lower and upper expectations with respect to sets of probability distributions. Our focus here is on the reuse and the combination of results obtained in previous iteration steps of the algorithm.

Cite this Paper


BibTeX
@InProceedings{pmlr-v103-fetz19a, title = {Improving the Convergence of Iterative Importance Sampling for Computing Upper and Lower Expectations}, author = {Fetz, Thomas}, booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {185--193}, year = {2019}, editor = {De Bock, Jasper and de Campos, Cassio P. and de Cooman, Gert and Quaeghebeur, Erik and Wheeler, Gregory}, volume = {103}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v103/fetz19a/fetz19a.pdf}, url = {http://proceedings.mlr.press/v103/fetz19a.html}, abstract = {The aim of this paper is to present methods for improving the convergence of an iterative importance sampling algorithm for calculating lower and upper expectations with respect to sets of probability distributions. Our focus here is on the reuse and the combination of results obtained in previous iteration steps of the algorithm.} }
Endnote
%0 Conference Paper %T Improving the Convergence of Iterative Importance Sampling for Computing Upper and Lower Expectations %A Thomas Fetz %B Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2019 %E Jasper De Bock %E Cassio P. de Campos %E Gert de Cooman %E Erik Quaeghebeur %E Gregory Wheeler %F pmlr-v103-fetz19a %I PMLR %P 185--193 %U http://proceedings.mlr.press/v103/fetz19a.html %V 103 %X The aim of this paper is to present methods for improving the convergence of an iterative importance sampling algorithm for calculating lower and upper expectations with respect to sets of probability distributions. Our focus here is on the reuse and the combination of results obtained in previous iteration steps of the algorithm.
APA
Fetz, T.. (2019). Improving the Convergence of Iterative Importance Sampling for Computing Upper and Lower Expectations. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 103:185-193 Available from http://proceedings.mlr.press/v103/fetz19a.html.

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