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Relational Logistic Regression: The Directed Analog of Markov Logic Networks

Seyed Mehran Kazemi; David Buchman; Kristian Kersting; Sriraam Natarajan; David Poole
In: Statistical Relational Artificial Intelligence, Papers from the 2014 AAAI Workshop. AAAI Conference on Artificial Intelligence (AAAI-2014), July 27, Québec City, Québec, Canada, AAAI Technical Report, Vol. WS-14-13, AAAI, 2014.


Relational logistic regression (RLR) was presented at the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR-2014). RLR is the directed analogue of Markov logic networks. Whereas Markov logic networks define distributions in terms of weighted formulae, RLR defines conditional probabilities in terms of weighted formulae. They agree for the supervised learning case when all variables except a query leaf variable are observed. However, they are quite different in representing distributions. The KR-2014 paper defined the RLR formalism, defined canonical forms for RLR in terms of positive conjunctive (or disjunctive) formulae, indicated the class of conditional probability distributions that can and cannot be represented by RLR, and defined many other aggregators in terms of RLR. In this paper, we summarize these results and compare RLR to Markov logic networks.

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