Project Summary
CrossCat is a domain-general, Bayesian method for analyzing high-dimensional data tables. CrossCat estimates the full joint distribution over the variables in the table from the data via approximate inference in a hierarchical, nonparametric Bayesian model, and provides efficient samplers for every conditional distribution. CrossCat combines strengths of nonparametric mixture modeling and Bayesian network structure learning: it can model any joint distribution given enough data by positing latent variables, but also discovers independencies between the observable variables.
In a Nutshell, crosscat...
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took an estimated 5 years of effort (COCOMO model)
starting with its first commit in November, 2012
ending with its most recent commit almost 6 years ago
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These details are provided for information only. No information here is legal advice and should not be used as such.
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