---
title: "Lifted First-Order Probabilistic Inference"
authors:
  - "Rodrigo de Salvo Braz"
  - "Eyal Amir"
  - "Dan Roth"
doi: ""
url: "https://ijcai.org/proceedings/2005/204"
pdf: "https://ijcai.org/proceedings/2005/0204.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "1319-1324"
track: ""
keywords:
---

# Lifted First-Order Probabilistic Inference

Rodrigo de Salvo Braz, Eyal Amir, Dan Roth

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 1319-1324.

## Abstract

Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting first-order specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poole, 2003] presented a method to perform inference directly on the first-order level, but this method is limited to special cases. In this paper we present the first exact inference algorithm that operates directly on a first-order level, and that can be applied to any first-order model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference.