
Lifted Inference for Relational Continuous Models
Relational Continuous Models (RCMs) represent joint probability densitie...
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Lifted Marginal MAP Inference
Lifted inference reduces the complexity of inference in relational proba...
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Lifted Hybrid Variational Inference
A variety of lifted inference algorithms, which exploit model symmetry t...
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Variational Probabilistic Inference and the QMRDT Network
We describe a variational approximation method for efficient inference i...
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A Complete Characterization of Projectivity for Statistical Relational Models
A generative probabilistic model for relational data consists of a famil...
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Inference, Learning, and Population Size: Projectivity for SRL Models
A subtle difference between propositional and relational data is that in...
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Mixed membership stochastic blockmodels
Observations consisting of measurements on relationships for pairs of ob...
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Lifted Relational Variational Inference
Hybrid continuousdiscrete models naturally represent many realworld applications in robotics, finance, and environmental engineering. Inference with largescale models is challenging because relational structures deteriorate rapidly during inference with observations. The main contribution of this paper is an efficient relational variational inference algorithm that factors largescale probability models into simpler variational models, composed of mixtures of iid (Bernoulli) random variables. The algorithm takes probability relational models of largescale hybrid systems and converts them to a closetooptimal variational models. Then, it efficiently calculates marginal probabilities on the variational models by using a latent (or lifted) variable elimination or a lifted stochastic sampling. This inference is unique because it maintains the relational structure upon individual observations and during inference steps.
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