# CREPO: An Open Repository to Benchmark Credal Network Algorithms

arXiv: https://arxiv.org/abs/2105.04158 · HTML: https://mlsanity.com/paper/2105.04158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 3 of 3)

- [Structural Causal Models Are (Solvable by) Credal Networks](https://mlsanity.com/paper/2008.00463/) (2020)
- [mplrs: A scalable parallel vertex/facet enumeration code](https://mlsanity.com/paper/1511.06487/) (2015)
- [Credal Networks under Epistemic Irrelevance](https://mlsanity.com/paper/1701.08661/) (2017)

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

- [Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits](https://mlsanity.com/paper/2205.05793/) (2022)

## Abstract (arXiv)

Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. Despite the NP-hardness of the (exact) task, a number of algorithms is available to approximate credal network inferences. In this paper we present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models. A Python tool is also delivered to load these data and interact with CREMA, thus making extremely easy to evaluate and compare existing and novel inference algorithms. To demonstrate such benchmarking scheme, we propose an approximate heuristic to be used inside variable elimination schemes to keep a bound on the maximum number of vertices generated during the combination step. A CREPO-based validation against approximate procedures based on linearization and exact techniques performed in CREMA is finally discussed.