Marginal Inference in Continuous Markov Random Fields Using Mixtures

Authors

  • Yuanzhen Guo The University of Texas at Dallas
  • Hao Xiong The University of Texas at Dallas
  • Nicholas Ruozzi The University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaai.v33i01.33017834

Abstract

Exact marginal inference in continuous graphical models is computationally challenging outside of a few special cases. Existing work on approximate inference has focused on approximately computing the messages as part of the loopy belief propagation algorithm either via sampling methods or moment matching relaxations. In this work, we present an alternative family of approximations that, instead of approximating the messages, approximates the beliefs in the continuous Bethe free energy using mixture distributions. We show that these types of approximations can be combined with numerical quadrature to yield algorithms with both theoretical guarantees on the quality of the approximation and significantly better practical performance in a variety of applications that are challenging for current state-of-the-art methods.

Downloads

Published

2019-07-17

How to Cite

Guo, Y., Xiong, H., & Ruozzi, N. (2019). Marginal Inference in Continuous Markov Random Fields Using Mixtures. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7834-7841. https://doi.org/10.1609/aaai.v33i01.33017834

Issue

Section

AAAI Technical Track: Reasoning under Uncertainty