2013/02/06 by Radim Jirousek, Radim Jiroušek, Jirousek, Radim
Computer Science · #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Bayesian probability #Composition (language) #Computer science #Data Management and Algorithms #FOS: Computer and information sciences #Graph #Probabilistic logic #Rough Sets and Fuzzy Logic #Statistical model #Terminology #Theoretical computer science #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.1551
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)
arxiv created 2013/02/06 · openalex publication_date 2013/02/06 · arxiv updated 2013/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Decomposable models and Bayesian networks can be defined as sequences of oligo-dimensional probability measures connected with operators of composition. The preliminary results suggest that the probabilistic models allowing for effective computational procedures are represented by sequences possessing a special property; we shall call them perfect sequences. The paper lays down the elementary foundation necessary for further study of iterative application of operators of composition. We believe to develop a technique describing several graph models in a unifying way. We are convinced that practically all theoretical results and procedures connected with decomposable models and Bayesian networks can be translated into the terminology introduced in this paper. For example, complexity of computational procedures in these models is closely dependent on possibility to change the ordering of oligo-dimensional measures defining the model. Therefore, in this paper, lot of attention is paid to possibility to change ordering of the operators of composition.