In the probabilistic approach to uncertainty management the input knowledge is usually represented by means of some probability distributions. In this paper we assume that the input knowledge is given by two discrete conditional probability distributions, represented by two stochastic matrices P and Q. The consistency of the knowledge base is analyzed. Coherence conditions and explicit formulas for the extension to marginal distributions are obtained in some special cases.
Cognitive-Motor Integration in Assessing Bimanual Motor Skills
College of Engineering, Florida A&M University and The Florida State University（佛罗里达农工大学与佛罗里达州立大学工程学院）；Biomedical Engineering Department, Rensselaer Polytechnic Institute（伦斯勒理工学院生物医学工程系）
Statistically Valid Variable Importance Assessment through Conditional Permutations
Inria（法国国家信息与自动化研究所）；Universite Paris Saclay（巴黎萨克雷大学）；CEA（法国原子能委员会）；Roche Pharma Research and Early Development（罗氏制药研究与早期开发部）；Neuroscience and Rare Diseases（神经科学与罕见病部门）；Roche Innovation Center Basel（罗氏巴塞尔创新中心）；F. Hoffmann–La Roche Ltd.（豪夫迈·罗氏有限公司）
Community-Led AI Integration for Wildfire Risk Assessment: A Participatory AI Literacy and Explainability Integration (PALEI) Framework in Los Angeles, CA
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；California State University, Los Angeles（加州州立大学洛杉矶分校）
DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment
Department of Computer Science and Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Computational Biology Branch, National Library of Medicine（国家医学图书馆计算生物学分支）；Khoury College of Computer Sciences, Northeastern University（东北大学计算机科学学院）；State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University（南京大学新型软件技术国家重点实验室，南京大学计算机科学学院）；Developmental Therapeutics Branch, National Cancer Institute（国家癌症研究所发育治疗分支）
