A computational scheme for Reasoning in Dynamic Probabilistic Networks

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 tha...

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Bibliographic Details
Published in:arXiv.org 2013-03
Main Author: Kjærulff, Uffe
Format: Article
Language:English
Subjects:
Approximation
Computation
Dynamical systems
Economic models
Nonlinear systems
Reasoning
Time series
Online Access:Get full text
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Summary: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.
ISSN:2331-8422