A Computational Scheme for Reasoning in Dynamic Probabilistic Networks
نویسنده
چکیده
A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegel halter {1988), and may be viewed as a gen eralization of the inference methods of clas sical time-series analysis in the sense that it allows description of non-linear, multi variate dynamic systems with complex con ditional independence structures. Further , the scheme provides a met hod for efficient backward smoothing and possibilities for effi cient, approximate forecasting methods. The scheme has been implemented on top of the HUGIN shell.
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