# Generating and Sampling Orbits for Lifted Probabilistic Inference

arXiv: https://arxiv.org/abs/1903.04672 · HTML: https://mlsanity.com/paper/1903.04672/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Colour Passing Revisited: Lifted Model Construction with Commutative Factors](https://mlsanity.com/paper/2309.11236/) (2023)
- [Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress](https://mlsanity.com/paper/2110.09152/) (2021)
- [Combining Local Symmetry Exploitation and Reinforcement Learning for Optimised Probabilistic Inference -- A Work In Progress](https://mlsanity.com/paper/2503.08786/) (2025)

## Abstract (arXiv)

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.