Stochastic Optimization for Numerical Evaluation of Imprecise Probabilities
Abstract
In applications of imprecise probability, analysts must compute lower (or upper) expectations, defined as the infimum of an expectation over a set of parameter values. Monte Carlo methods consistently approximate expectations at fixed parameter values, but can be costly to implement in grid search to locate minima over large subsets of the parameter space. We investigate the use of stochastic iterative root-finding methods for efficiently computing lower expectations. In two examples we illustrate the use of various stochastic approximation methods, and demonstrate their superior performance in comparison to grid search.
Cite
Text
Syring and Martin. "Stochastic Optimization for Numerical Evaluation of Imprecise Probabilities." Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, 2021.Markdown
[Syring and Martin. "Stochastic Optimization for Numerical Evaluation of Imprecise Probabilities." Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, 2021.](https://mlanthology.org/isipta/2021/syring2021isipta-stochastic/)BibTeX
@inproceedings{syring2021isipta-stochastic,
title = {{Stochastic Optimization for Numerical Evaluation of Imprecise Probabilities}},
author = {Syring, Nicholas and Martin, Ryan},
booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications},
year = {2021},
pages = {289-298},
volume = {147},
url = {https://mlanthology.org/isipta/2021/syring2021isipta-stochastic/}
}