Uncertainty Quantification¶
Uncertainty quantification refers to the mathematical and computational methods for representing, characterizing, and reasoning about uncertainty in complex systems and decision-making processes. In the context of misinformation detection, uncertainty quantification is essential for modeling the confidence in claim veracity assessments, source reliability, and evidence quality.
Key approaches include Bayesian probability, Dempster-Shafer theory, fuzzy logic, and interval analysis. Each framework has different strengths for handling various types of uncertainty—aleatoric (random variability) and epistemic (lack of knowledge).
Key papers¶
- Belief Evolution Network-based Probability Transformation and Fusion — advances in probability transformation for decision-making under uncertainty
Related topics¶
- Dempster-Shafer Theory (specific framework)
- Belief Functions (mathematical tools)
- Information Fusion (applied methodology)