
Factor Graph Grammars
We propose the use of hyperedge replacement graph grammars for factor gr...
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Lifted Probabilistic Inference for Asymmetric Graphical Models
Lifted probabilistic inference algorithms have been successfully applied...
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Contextual Symmetries in Probabilistic Graphical Models
An important approach for efficient inference in probabilistic graphical...
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Distributed Parallel Inference on Large Factor Graphs
As computer clusters become more common and the size of the problems enc...
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Translating Recursive Probabilistic Programs to Factor Graph Grammars
It is natural for probabilistic programs to use conditionals to express ...
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Strudel: Learning StructuredDecomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a c...
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Exploiting Uniform Assignments in FirstOrder MPE
The MPE (Most Probable Explanation) query plays an important role in pro...
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Generating and Sampling Orbits for Lifted Probabilistic Inference
Lifted inference scales to large probability models by exploiting symmetry. However, existing exact lifted inference techniques do not apply to general factor graphs, as they require a relational representation. In this work we provide a theoretical framework and algorithm for performing exact lifted inference on symmetric factor graphs by computing colored graph automorphisms, as is often done for approximate lifted inference. Our key insight is to represent variable assignments directly in the colored factor graph encoding. This allows us to generate representatives and compute the size of each orbit of the symmetric distribution. In addition to exact inference, we use this encoding to implement an MCMC algorithm that explores the space of orbits quickly by uniform orbit sampling.
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