# Lifting Factor Graphs with Some Unknown Factors

arXiv: https://arxiv.org/abs/2406.01275 · HTML: https://mlsanity.com/paper/2406.01275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference](https://mlsanity.com/paper/1401.1247/) (2014)
- [Lifted Variable Elimination: Decoupling the Operators from the Constraint Language](https://mlsanity.com/paper/1402.0565/) (2014)
- [Colour Passing Revisited: Lifted Model Construction with Commutative Factors](https://mlsanity.com/paper/2309.11236/) (2023)
- [Taming Reasoning in Temporal Probabilistic Relational Models](https://mlsanity.com/paper/1911.07040/) (2019)
- [Efficient Detection of Exchangeable Factors in Factor Graphs](https://mlsanity.com/paper/2403.10167/) (2024)
- [Lifted Causal Inference in Relational Domains](https://mlsanity.com/paper/2403.10184/) (2024)

## Built on by (most cited first, 1 of 1)

- [Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs](https://mlsanity.com/paper/2411.07006/) (2024)

## Abstract (arXiv)

Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing factors whose potentials are unknown. We introduce the Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify symmetric subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics and allow for (lifted) probabilistic inference.