Inproceedings,

Monte Carlo Estimation for Imprecise Probabilities: Basic Properties

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Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, volume 103 of Proceedings of Machine Learning Research, page 135--144. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)

Abstract

We describe Monte Carlo methods for estimating lower envelopes of expectations of real random variables. We prove that the estimation bias is negative and that its absolute value shrinks with increasing sample size. We discuss fairly practical techniques for proving strong consistency of the estimators and use these to prove the consistency of an example in the literature. We also provide an example where there is no consistency.

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