  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.

Machine Learning;Machine Learning