BayesDB for Easier Analysis
December 18, 2013
Interesting—it seems the venerated Thomas Bayes is now with us in database land. BayesDB is being developed, in conjunction with an analysis method called CrossCat, by a team of folks from MIT‘s Probabilistic Computing Project and the Shafto Lab at the University of Louisville.
The project’s page explains:
“BayesDB, a Bayesian database table, lets users query the probable implications of their data as easily as a SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries.
BayesDB is suitable for analyzing complex, heterogeneous data tables with up to tens of thousands of rows and hundreds of variables. No preprocessing or parameter adjustment is required, though experts can override BayesDB’s default assumptions when appropriate.
BayesDB’s inferences are based in part on CrossCat, a new, nonparametric Bayesian machine learning method, that automatically estimates the full joint distribution behind arbitrary data tables.”
The database is designed for two types of folks: those with no statistics chops who nonetheless have tabular data to analyze, and those proficient with statistics who have a non-standard problem or who have no time or patience for custom modeling. The team credits CrossCat in part with making BayesDB possible, but also say the BQL language was key to its development.
The description includes examples, a discussion of which types of data and problems the database addresses best, reasons to trust the results, why they named it BayesDB, and more. Check out the page for all the details.
Cynthia Murrell, December 18, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
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