Possibility Theory

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9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making
Oct 29, 2026Seyedfakhredin Musavishavazi, Maryam AlipourPossibility Theory Uncertainty Quantification
Federal Institute for Occupational Safety and Health, Fabricestraße 8, 01099 Dresden, Germany
Open-ended Scientific Discovery with Possibilistic Reasoning
Oct 8, 2026Anita Yang, Siu Lun Chau, Tomoya Wakayama +2Possibility Theory Scientific Discovery
Lattice Lab, Toyota Motor Corporation, Japan · Department of Computer Science, University of Tokyo, Japan · EPIC Lab, College of Computing & Data Science, Nanyang Technological University, Singapore +2
Evidential Information Fusion on Possibilistic Structure
May 16, 2026Qianli Zhou, Ye Cui, Zhen Li +2Possibility Theory Dempster-Shafer Theory
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
Possibilistic Predictive Uncertainty for Deep Learning
May 1, 2026Yao Ni, Jeremie Houssineau, Yew-Soon Ong +1Possibility Theory Uncertainty Quantification
Nanyang Technological University · A*STAR · The University of New South Wales +1
The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence
Mar 13, 2026Moriba Kemessia JahPossibility Theory Imprecise Probability
Black Swan Research Group, GaiaVerse, Ltd., Austin, TX 78728, USA · Jah Decision Intelligence Group, Aerospace Engineering & Engineering Mechanics The University of Texas at Austin, Austin, TX 78712
The Epistemic Support-Point Filter: Jaynesian Maximum Entropy Meets Popperian Falsification
Mar 10, 2026Moriba Kemessia JahEpistemic Uncertainty Possibility Theory
Black Swan Research Group, GaiaVerse, Ltd. · Jah Decision Intelligence Group, Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin
Factorizing formal contexts from closures of necessity operators
Feb 25, 2026Roberto G. Aragón, Jesús Medina, Eloísa Ramírez-PoussaPossibility Theory Formal Concept Analysis
Department of Mathematics, University of Cádiz. Spain
Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems
Feb 17, 2026Volodymyr YuzefovychPossibility Theory Data Integration
Institute for Information Recording of the National Academy of Sciences of Ukraine, 2 Mykoly Shpaka St, Kyiv, Ukraine.
A Concept of Possibility for Real-World Events
Oct 3, 2025Daniel G. SchwartzPossibility Theory
Florida State University, USA

Oct 29, 2026J/K move · Enter open · S save

Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making

Seyedfakhredin Musavishavazi, Maryam Alipour

Federal Institute for Occupational Safety and Health, Fabricestraße 8, 01099 Dresden, Germany

Possibility Theory Uncertainty Quantification Decision-Making under Uncertainty Bayesian Inference

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Decision-making under uncertainty remains a key issue in Machine Learning (ML), particularly in the deployment of safety-critical systems, where reliable quantification of system behavior is essential for evaluating behavioral impacts and strengthening confidence in ML models. This contribution, influenced by fuzzy set theory and grounded in Bayesian statistics, addresses challenges related to data precision and the performance-accuracy trade-off in Bayesian inference (BI). To this end, we introduce a possibility-based contrastive grade of prediction as an alternative quantification method to existing latent posterior-based approaches for parameter estimation (approximation) and model selection. Unlike conventional Bayesian inference, which represents uncertainty within the probabilistic framework, the proposed model adopts a possibility-based representation that allows uncertain and potentially conflicting information to be characterized more flexibly. Rather than focusing on isolated aspects of a problem, the resulting mathematical framework provides a systematic approach to data mining while mitigating overly confident, yet implausible predictions generated by highly specialized deep learning architectures.