Bayesian data analysis
Jump to navigation
Jump to search
The goal of Bayesian [1] or integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement. [2][3] Like Function parametrization, this technique requires having a model to predict the measurement readings for any given state of the physical system; however, the handling of error propagation is more sophisticated with the Bayesian method, and additionally, it provides a systematic way to include prior knowledge into the analysis. [4]
References
- ↑ D.S. Sivia, Data Analysis: A Bayesian Tutorial, Oxford University Press, USA (1996) ISBN 0198518897
- ↑ R. Fischer, A. Dinklage, Integrated data analysis of fusion diagnostics by means of the Bayesian probability theory, Rev. Sci. Instrum. 75 (2004) 4237
- ↑ J. Svensson, A. Werner, Large Scale Bayesian Data Analysis for Nuclear Fusion Experiments, IEEE International Symposium on Intelligent Signal Processing (2007) 1
- ↑ R. Fischer, A. Dinklage, and E. Pasch, Bayesian modelling of fusion diagnostics, Plasma Phys. Control. Fusion 45 (2003) 1095-1111