| Contents: |
Introduction -- Graphical models in a nutshell -- Inductive logic programming in a nutshell -- An introduction to conditional random fields for relational learning -- Probabilistic relational models -- Relational Markov networks -- Probabilistic entity-relationship models, PRMs, and plate models -- Relational dependency networks -- Logic-based formalisms for statistical relational learning -- Bayesian logic programming: theory and tool -- Stochastic logic programs: a tutorial -- Markov logic: a unifying framework for statistical relational learning -- BLOG: probabilistic models with unknown objects -- The design and implementation of IBAL: a general-purpose probabilistic language -- Lifted first-order probabilistic inference -- Feature generation and selection in multi-relational statistical learning -- Learning a new view of a data base: with an application in mammography -- Reinforcement learning in relational domains: a policy-language approach -- Statistical relational learning for natural language information extraction -- Global inference for entity and relation identification via a linear programming formulation. |
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