Dempster-Shafer Theory

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Overview

Dempster–Shafer theory, also called evidence theory, represents support for sets of possible answers rather than requiring precise probabilities, allowing it to express both uncertainty and ignorance. Its main tools include mass assignments over subsets, belief and plausibility bounds, and combination rules such as Dempster’s rule; newer approaches use reliability discounting and evidential deep learning. Applications include sensor and multimodal fusion, medical question answering, and survival prediction, while methods such as Mondrian conformal calibration address uncertainty when inputs are missing. A central tension is that results depend on how evidence and source reliability are encoded, and conflict-aware fusion does not consistently outperform probability averaging, making calibration and fair comparisons important.

Latest papers

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12 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Neural Networks with Differentiable Belief-Function Layers

    Oct 18, 2026Antonin SulcEvidential Deep LearningEpistemic Uncertainty

  2. Evidential Deep Learning for Multi-Modal Anti-UAV Detection

    Sep 1, 2026Dmitry Golovchits, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor AlsahagEvidential Deep LearningDempster-Shafer Theory

    University of Amsterdam Amsterdam, The Netherlands

  3. Uncertainty-Aware Multimodal Anti-UAV Detection via Evidential Fusion and Conflict-Discounted Belief Aggregation

    Aug 29, 2026Sharanda Suttorp, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansour AlsahagUncertainty-Aware Multimodal FusionAerial Object Detection

    University of Amsterdam Amsterdam, The Netherlands · Department of Computer Science and Technology, SUNY Empire State University Saratoga Springs, NY, USA

  4. Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting

    Aug 13, 2026Huiyu Li, Weibo Liu, Xinru Xu +3Dempster-Shafer Theory

    School of Business, Central South University, Changsha, Hunan, People’s Republic of China, 410083 · School of Management, Shandong University, Jinan, Shandong, People’s Republic of China, 250100

  5. Decision-Aware Approximation of Belief Functions for Evidential Combinatorial Optimization

    Aug 11, 2026Sohaib AfifiDempster-Shafer TheoryCombinatorial Optimization

    Univ. Artois, UR 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), Béthune, France

  6. Conformal Fusion Under Missing Modalities

    Aug 7, 2026Alireza MoayedikiaMissing-Modality LearningGroup-Conditional Conformal Prediction

    Department of Business Technology and Entrepreneurship Swinburne University of Technology Hawthorn, VIC 3122, Australia

  7. Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities

    Jun 18, 2026Yucheng Xing, Hailan Mo, Zi Wang +2Missing-Modality LearningUncertainty Quantification

    National University of Singapore, Singapore · Imperial College London, London, UK

  8. Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling

    Jun 14, 2026Yuhan Wang, Manqing Wang, Yixuan Lu +2Uncertainty QuantificationDempster-Shafer Theory

    Columbia University New York, USA · Trine University Phoenix, USA · University of Sofia Palo Alto, USA +2

  9. BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering

    May 17, 2026Chang Zong, Hao Ning, Siliang Tang +2HealthcareEvidence-Grounded Reasoning

    School of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China · College of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China · Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China

  10. Evidential Information Fusion on Possibilistic Structure

    May 16, 2026Qianli Zhou, Ye Cui, Zhen Li +2Dempster-Shafer TheoryDecision-Making under Uncertainty

    School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China · Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada · China Mobile Information Technology Center, Beijing, 100029, China +4

  11. Statistical inference with belief functions: A survey

    May 8, 2026Fabio CuzzolinDempster-Shafer Theory

    Institute for AI, Data Analysis and Systems (AIDAS) · School of Engineering, Computing & Mathematics, Oxford Brookes University

  12. Equivalence and Divergence of Bayesian Log-Odds and Dempster's Combination Rule for 2D Occupancy Grids

    Feb 21, 2026Tatiana Berlenko, Kirill KrinkinOccupancy Grid MappingDempster-Shafer Theory

    Constructor University, Bremen, Germany · JetBrains LTD