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Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring




We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise graphical models with strictly positive factors that requires almost no preprocessing and is easy to implement. Moreover, we show how our method can be combined with blocking to improve mixing. Even though our method leads to inferior mixing times compared to a sequential Gibbs sampler, we argue that our method is still very useful for large dynamic networks, where factors are added and removed on a continuous basis, as it is hard to maintain a graph coloring in this setup. Similarly, our method is useful for parallelizing Gibbs sampling in graphical models that do not allow for graph colorings with a small number of colors such as densely connected graphs.

Author(s): L. Mescheder and S. Nowozin and A. Geiger
Journal: Arxiv
Year: 2016

Department(s): Autonomous Vision
Bibtex Type: Article (article)
Paper Type: Technical Report

Links: pdf


  title = {Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring},
  author = {Mescheder, L. and Nowozin, S. and Geiger, A.},
  journal = {Arxiv},
  year = {2016}