Density Estimation via Bayesian Inference Engines
This work provides a fully Bayesian method for density estimation with credible intervals, which is incremental as it builds on existing inference engines.
The authors tackled the problem of probability density function estimation by leveraging modern Bayesian inference engines like no-U-turn sampling and expectation propagation, achieving excellent comparative performance and scalability to large sample sizes through a binning strategy.
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.