Density Estimation via Bayesian Inference Engines

M. P. Wand; J. C. F. Yu

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arXiv · 2009.06182

Density Estimation via Bayesian Inference Engines

M. P. Wand·J. C. F. Yu

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Abstract

We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due to a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by pointwise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.

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M. P. Wand, J. C. F. Yu. 2020-09-14. Density Estimation via Bayesian Inference Engines. https://arxiv.org/abs/2009.06182

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