The concept of integrated data analysis of complementary experiments

The Integrated Data Analysis (IDA) concept allows one to combine data from different experiments to obtain improved results. Heterogeneous and complementary experimental data as well as various kinds of physical prior information are easily integrated employing Bayesian probability theory. The concepts of IDA are compared to the traditional approach for data analysis where sequential analysis and iterative schemes are usually found. In contrast to classical inversion techniques IDA needs only forward modeling and a thorough error assessment: The ingredients are given by a model linking the physical quantities of interest to the measured data, a statistical description of the measurements, and a probabilistic description of all nuisance model parameters suffering from uncertainties. In practice, the probabilistic description of systematic measurement and model uncertainties are of major importance to resolve data inconsistencies. Complex error propagation is obtained automatically combining data in a concise probabilistic one-step analysis.