Generating and Sampling Orbits for Lifted Probabilistic Inference
Artificial Intelligence
2019-07-02 v3
Abstract
A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non-relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.
Cite
@article{arxiv.1903.04672,
title = {Generating and Sampling Orbits for Lifted Probabilistic Inference},
author = {Steven Holtzen and Todd Millstein and Guy Van den Broeck},
journal= {arXiv preprint arXiv:1903.04672},
year = {2019}
}