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A Bayesian Motivated Laplace Inversion for Multivariate Probability Distributions

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The paper introduces a recursive procedure to invert the multivariate Laplace transform of probability distributions. The procedure involves taking independent samples from the Laplace transform; these samples are then used to update recursively an initial starting distribution. The update is Bayesian driven. The final estimate can be written as a mixture of independent gamma distributions, making it the only methodology which guarantees to numerically recover a probability distribution with positive support. Proof of convergence is given by a fixed point argument. The estimator is fast, accurate and can be run in parallel since the target distribution is evaluated on a grid of points. The method is illustrated on several examples and compared to the bivariate Gaver–Stehfest method.

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The authors are grateful for the comments and suggestions of two reviewers and the Editor on a previous version of the paper. The first author is partially funded by Cariplo. The second author is partially funded by NSF grant DMS 1612891.

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Correspondence to Lorenzo Cappello.

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Cappello, L., Walker, S.G. A Bayesian Motivated Laplace Inversion for Multivariate Probability Distributions. Methodol Comput Appl Probab 20, 777–797 (2018).

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