Titlebook: Medical Data Analysis; First International Rüdiger W. Brause,Ernst Hanisch Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 2

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Deconvolution and Credible Intervals using Markov Chain Monte Carlo Methodily due to ill conditioning. To overcome these problems, deconvolution was formulated using a stochastic approach within the Bayesian modelling framework. The stochastic deconvolution with a piece-wise constant definition of the signal (the input function) cannot be solved analytically but the solut

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Graphical Explanation in Bayesian Networkscausal knowledge and statistical data. However, a condition for the acceptance of a medical expert system is the ability to explain the diagnosis. This is a difficult task, because probabilistic inference seems to have little relation with human thinking. The current paper focuses on the graphic int

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Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Mapsbut not verified topology preservation is in the main focus. Extensions of the usual SOM are offered to obtain correct results. The authors give examples for applications such as visualization and clustering.

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Analysis of Nonlinear Differential Equations: Parameter Estimation and Model Selectionh respect to reliability and effectiveness. The method is based on the multiple shooting approach introduced by Bock (1983). Different models are compared with respect to their adequacy by means of likelihood ratio tests. We demonstrate the advantages of the algorithm with simulated data and present

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Basilio Sierra,Iñaki Inza,Pedro Larrañagal assume in this paper that . is finite, and that there is a list of . objects defining this population. The objects are of m different types. Suppose there are .. objects of type j, and define the . π. = ../.. Now consider the set of all sequences of length N, with elements from .. There are .. suc

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