Integrated Data Analysis for Fusion: A Bayesian Tutorial for Fusion Diagnosticians
A. Dinklage, Heiko Dreier, Rainer Dieter Fischer, Silvio Gori, R. Preuss, U. von Toussaint, G. Gorini, Francesco Paolo Orsitto, Elio Sindoni, Marco Tardocchi · AIP conference proceedings · 2008
Integrated Data Analysis (IDA) offers a unified way of combining information relevant to fusion experiments. Thereby, IDA meets with typical issues arising in fusion data analysis. In IDA, all information is consistently formulated as probability density functions quantifying uncertainties in the analysis within the Bayesian probability theory. For a single diagnostic, IDA allows the identification of faulty measurements and improvements in the setup. For a set of diagnostics, IDA gives joint error distributions allowing the comparison and integration of different diagnostics results. Validation of physics models can be performed by model comparison techniques. Typical data analysis applications benefit from IDA capabilities of nonlinear error propagation, the inclusion of systematic effects and the comparison of different physics models. Applications range from outlier detection, background discrimination, model assessment and design of diagnostics. In order to cope with next step fusion device requirements, appropriate techniques are explored for fast analysis applications.