Density estimation via Bayesian inference engines

M. P. Wand and J. C. F. Yu ()
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M. P. Wand: University of Technology Sydney
J. C. F. Yu: University of Technology Sydney

AStA Advances in Statistical Analysis, 2022, vol. 106, issue 2, No 2, 199-216

Abstract: 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 point-wise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.

Keywords: Expectation propagation; Mixed model-based penalized splines; No-U-turn sampler; Semiparametric mean field variational Bayes; Slice sampling (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10182-021-00422-8

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