mlsanityData to 14 Oct 2026

Statistical inference with belief functions: A survey

arXiv 2605.07908 ↗First posted 8 May 2026math.ST cs.AI cs.LGcited by 2

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Abstract (arXiv)

Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack of data renders learning a probability distribution for the problem impractical. The first step in a reasoning chain based on belief functions is inference: how to learn a belief measure from the available data. In this survey we focus, in particular, on making inference from statistical data, and review the most significant contributions in the area.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 62 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

8 of 62 references.

References · 8

  1. 2018Visions of a generalized probability theorycited by 13
  2. 2025A Unified Evaluation Framework for Epistemic Predictionscited by 10
  3. 2021Uncertainty measures: The big picturecited by 8
  4. 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'cited by 8
  5. 2025Credal and Interval Deep Evidential Classificationscited by 8
  6. 2023Reasoning with random sets: An agenda for the futurecited by 6
  7. 2018Belief likelihood function for generalised logistic regressioncited by 5
  8. 2024Generalising realisability in statistical learning theory under epistemic uncertaintycited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Credal Large Language Models for Semantic Commitment under Uncertaintycited by 2
  2. 2026XU-RS: Explaining Credal Width in Random-Set Language Modelscited by 0

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  1. 2024The Quantified Boolean Bayesian Network: Theory and Experiments with a Logical Graphical Model
  2. 2025Martingale Score: An Unsupervised Metric for Bayesian Rationality in LLM Reasoning
  3. 2026Proportional Analogies on Probability Distributions via Bayesian Updating
  4. 2022Towards Unifying Logical Entailment and Statistical Estimation
  5. 2026Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
  6. 2026How Uncertainty Estimation Scales with Sampling in Reasoning Models
  7. 2022A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal Reasoning
  8. 2023A Simple Generative Model of Logical Reasoning and Statistical Learning