# Taming Reasoning in Temporal Probabilistic Relational Models

arXiv: https://arxiv.org/abs/1911.07040 · HTML: https://mlsanity.com/paper/1911.07040/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Colour Passing Revisited: Lifted Model Construction with Commutative Factors](https://mlsanity.com/paper/2309.11236/) (2023)
- [Lifted Causal Inference in Relational Domains](https://mlsanity.com/paper/2403.10184/) (2024)
- [Lifting Factor Graphs with Some Unknown Factors](https://mlsanity.com/paper/2406.01275/) (2024)
- [Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs](https://mlsanity.com/paper/2411.11730/) (2024)
- [On the Completeness and Complexity of the Lifted Dynamic Junction Tree Algorithm](https://mlsanity.com/paper/2110.09197/) (2021)
- [Lifting Factor Graphs with Some Unknown Factors for New Individuals](https://mlsanity.com/paper/2504.04089/) (2025)
- [Lifted Causal Inference](https://mlsanity.com/paper/2606.28024/) (2026)

## Abstract (arXiv)

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by restoring a lifted representation, we present temporal approximate merging (TAMe), which incorporates (i) clustering for grouping submodels as well as (ii) statistical significance checks to test the fitness of the clustering outcome. In exchange for faster runtimes, TAMe introduces a bounded error that becomes negligible over time. Empirical results show that TAMe significantly improves the runtime performance of inference, while keeping errors small.