2013/02/13 by Constantin Aliferis, Constantin F. Aliferis, Aliferis, Constantin F. +2 · 38 citations
Computer Science · Mathematics · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Bayesian probability #Computer science #Econometrics #FOS: Computer and information sciences #Mathematics #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.3552
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)
arxiv created 2013/02/13 · openalex publication_date 2013/02/13 · arxiv updated 2013/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a structural and temporal extension of Bayesian Belief Networks (BNs) to facilitate normative temporal and causal modeling under uncertainty. In this paper we present definitions of the model, its components, and its fundamental properties. We also discuss how to represent various types of temporal knowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamic structures, avoiding causal temporal inconsistencies, and dealing with models that involve simultaneously actions (decisions) and causal and non-causal associations. We examine the relationships among BNs, Modifiable Belief Networks, and MTBNs with a single temporal granularity, and suggest areas of application suitable to each one of them.