# Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks

arXiv: https://arxiv.org/abs/1802.05639 · HTML: https://mlsanity.com/paper/1802.05639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

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

- [A New Score for Adaptive Tests in Bayesian and Credal Networks](https://mlsanity.com/paper/2105.12205/) (2021)

## Abstract (arXiv)

A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that evidence propagation in this setup can be reduced to standard updating in an augmented credal network, equivalent to a set of consistent Bayesian networks. A characterization of the computational complexity for this task is derived together with an efficient exact procedure for a subclass of instances. In the case of multiple uncertain evidences over the same variable, the proposed procedure can provide a set-valued version of the geometric approach to opinion pooling.