BayesDB

"Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality?

BayesDB is a probabilistic programming platform that enables users to query the probable implications of their data as directly as SQL databases enable them to query the data itself.

The default modeling assumptions that BayesDB makes are suitable for a broad class of problems, but statisticians can customize these assumptions when necessary. BayesDB also enables domain experts that lack statistical expertise to perform qualitative model checking and encode simple forms of qualitative prior knowledge.

The Bayesian Query Lanuage (BQL) allows analysts and domain experts to interact perform Bayesian data analysis without requiring a detailed understanding of model implementation. That means queries can be articulated before models have been build, and models can be improved and optimized without invalidating existing queries.

The Meta-modeling Language (MML) enables machine assisted modeling for populations based on samples and domain insight.  By specifying population schemas and also by using the MML, domain experts can encode qualitative prior knowledge and control the behavior of BayesDB's built-in model building engine."

http://probcomp.csail.mit.edu/bayesdb/

http://probcomp.csail.mit.edu/

https://github.com/probcomp/bayeslite