2013/03/13 by Uffe Kjærulff, Kjærulff, Uffe
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1303.5407
openalex publication_date 2013/03/13 · openalex created_date 2022/09/17 · openalex updated_date 2026/07/28
A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of the inference methods of classical time-series analysis in the sense that it allows description of non-linear, multivariate dynamic systems with complex conditional independence structures. Further, the scheme provides a method for efficient backward smoothing and possibilities for efficient, approximate forecasting methods. The scheme has been implemented on top of the HUGIN shell.