Explores optimization-based uncertainty quantification for ill-posed inverse problems in the physical sciences, focusing on regularization methods and interval constructions.
Covers detectors' types, counting statistics, error prediction, and uncertainty estimation in measurements, emphasizing the importance of statistical tests and the optimization of experiments.
