BayesDB: A probabilistic programming system for querying the probable
  implications of data
  Is it possible to make statistical inference broadly accessible to
non-statisticians without sacrificing mathematical rigor or inference quality?
This paper describes BayesDB, a probabilistic programming platform that aims to
enable users to query the probable implications of their data as directly as
SQL databases enable them to query the data itself. This paper focuses on four
aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data
analysis, that answers queries by averaging over an implicit space of
probabilistic models; (ii) techniques for implementing BQL using a broad class
of multivariate probabilistic models; (iii) a semi-parametric Bayesian
model-builder that auomatically builds ensembles of factorial mixture models to
serve as baselines; and (iv) MML, a "meta-modeling" language for imposing
qualitative constraints on the model-builder and combining baseline models with
custom algorithmic and statistical models that can be implemented in external
software. BayesDB is illustrated using three applications: cleaning and
exploring a public database of Earth satellites; assessing the evidence for
temporal dependence between macroeconomic indicators; and analyzing a salary
survey.
