A Note on Imprecise Monte Carlo over Credal Sets via Importance Sampling
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Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 62:325332, 2017.
Abstract
This brief paper is an exploratory investigation of how we can apply sensitivity analysis over importance sampling weights in order to obtain sampling estimates of lower previsions described by a parametric family of distributions. We demonstrate our results on the imprecise Dirichlet model, where we can compare with the analytically exact solution. We discuss the computational limitations of the approach, and propose a simple iterative importance sampling method in order to overcome these limitations. We find that the proposed method works pretty well, at least in the example studied, and we discuss some further possible extensions.
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