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 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.
