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Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data.
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization
In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains.
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes.
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