Dynamic Propagation in Causal Probabilistic Networks with Instantiated Variables

An extension to the Hugin tool for implementing dynamic propagation in causal probabilistic networks with some instantiated variables is presented. The extension makes it possible to combine a dynamically defined network, implemented without the use of Hugin, with a static network implemented in Hugin, thereby reducing the calculation time by several orders of magnitude compared to using Hugin alone. The application of the dynamic propagation in a Diabetes Advisory System, DIAS, for giving advise on insulin dose adjustment in insulin dependent diabetes mellitus, is described as an example.

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