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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 August 2008): Version 0.3 is now available. This version includes the lifted exact inference algorithm (C-FOVE) introduced in our AAAI-08 paper, as well as the standard variable elimination algorithm. It also allows users to include parfactors in BLOG files. These new features are (briefly) documented in the last two sections of the manual. This release also includes particle filtering and decayed MCMC implementations for Dynamic BLOG (DBLOG) models. See the change log for more details.