Density Estimation via Bayesian Inference Engines

Bayesian inference engines have become established as an important paradigm for inference in arbitrarily large and complex graphical models. Software platforms such as Infer.NET (Minka et al., 2018) and Stan (Carpenter et al., 2017) are instances of such Bayesian inference engines. They deliver approximate Bayesian inference, with varying degrees of inferential accuracy, by calling upon contemporary approaches such as expectation propagation, Hamiltonian Monte Carlo and variational approximation. The purpose of this short article is to show that effective and scalable probability density function estimation, or density estimation for short, can be achieved using Bayesian inference engines. We provide easy access for users of the R statistical computing environment (R Core Team, 2018) via a package named densEstBayes (Wand, 2020).