BayesDB: A probabilistic programming system for querying the probable implications of data

TL;DR

BayesDB is a probabilistic programming system that enables non-statisticians to perform Bayesian data analysis through an SQL-like language, facilitating broad access to statistical inference and data exploration.

Contribution

It introduces BQL, a query language for Bayesian analysis, and a semi-parametric model-builder that automatically creates probabilistic models, enhancing accessibility and flexibility.

Findings

Applied to satellite data, macroeconomic analysis, and salary surveys.

Demonstrated effective data cleaning and exploration capabilities.

Showed how Bayesian inference can be integrated into familiar query frameworks.

Abstract

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.