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Bayesian Logic (BLOG) Inference Engine

Bayesian logic (BLOG) is a first-order probabilistic modeling language under development at MIT and 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.

News (14 December 2007): Version 0.2 is now available. This version adds some syntactic conveniences (simultaneously declaring a random function and specifying its dependency model; overloading function symbols), makes a lot of improvements "under the hood", and fixes a number of bugs. See the change log for details.

Obtaining the BLOG Inference Engine

You can download the BLOG Inference Engine version 0.2 from the download page.

Documentation

BLOG Contributors

BLOG is being developed in Prof. Stuart Russell's research group at Berkeley and in Prof. Leslie Kaelbling's group at MIT. Contributors to the theoretical and programming aspects of the project include: Funding has been provided by the Defense Advanced Research Projects Agency (DARPA).