Density estimation via Bayesian inference engines

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 have excellent comparative performance scale well to very large sample sizes due a binning strategy. Moreover, approach is fully all are accompanied by point-wise credible intervals. An accompanying package in R language facilitates easy use of new estimates.