Bayesian Logic (BLOG) is a first-order probabilistic modeling language under development at UC Berkeley. It is designed for making inferences about real-world objects that underlie some observed data: for instance, tracking multiple people in a video sequence, or identifying repeated mentions of people and organizations in a set of text documents. BLOG makes it relatively easy to represent uncertainty about the number of underlying objects and the mapping between objects and observations.

Obtain the BLOG Engine

The latest release is  BLOG 0.7.  Please check the release note for the updates.

The language reference for this version can be downloaded from EECS-2013-51
Funding for BLOG has been provided by the Defense Advanced Research Projects Agency (DARPA).