Daniel L. Ong
Abstract: This chapter describes Bayesian logic (BLOG), a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing approaches. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model structures that can contain varying and unbounded numbers of objects. Furthermore, complete inference algorithms exist for a large fragment of the language. We also introduce a probabilistic form of Skolemization for handling evidence.
To appear in: Lise Getoor and Ben Taskar, eds. Statistical Relational Learning. Cambridge, MA: MIT Press.
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