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

Thomas Fetz
ISIPTA 2019 pp. 185-193
/isipta/2019/fetz2019isipta-improving/

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.

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Cite

Text

Fetz. "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, 2019.

Markdown

[Fetz. "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, 2019.](https://mlanthology.org/isipta/2019/fetz2019isipta-improving/)

BibTeX

@inproceedings{fetz2019isipta-improving,
  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},
  year      = {2019},
  pages     = {185-193},
  volume    = {103},
  url       = {https://mlanthology.org/isipta/2019/fetz2019isipta-improving/}
}