# The Linearization of Pairwise Markov Networks

@article{Gatterbauer2015TheLO, title={The Linearization of Pairwise Markov Networks}, author={Wolfgang Gatterbauer}, journal={ArXiv}, year={2015}, volume={abs/1502.04956} }

Belief Propagation (BP) allows to approximate exact probabilistic inference in graphical models, such as Markov networks (also called Markov random fields, or undirected graphical models). However, no exact convergence guarantees for BP are known, in general. Recent work has proposed to approximate BP by linearizing the update equations around default values for the special case when all edges in the Markov network carry the same symmetric, doubly stochastic potential. This linearization has…

## 3 Citations

### ZooBP: Belief Propagation for Heterogeneous Networks

- Computer ScienceProc. VLDB Endow.
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### Factorized Graph Representations for Semi-Supervised Learning from Sparse Data

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This work suggests a principled and scalable method for directly estimating the compatibilities from a sparsely labeled graph and refers to algebraic amplification as the underlying idea of leveraging algebraic properties of an algorithm's update equations to amplify sparse signals in data.

### Mining Anomalies using Static and Dynamic Graphs

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