Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables
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We introduce a novel unbiased estimator for the density of a sum of random variables. Our estimator possesses several advantages over the conditional Monte Carlo approach. Specifically, it applies to the case of dependent random variables, allows for transformations of random variables, is computationally faster to run, and is simpler to implement. We provide several numerical examples that illustrate these advantages.
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