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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.