Parameter estimation and uncertainty quantification

Experimental measurement data in biology are usually sparse and noisy. Therefore, we combine local optimization methods for parameter identification with new algorithmic approaches to Bayesian inverse problems. For example, we have suggested a new transformation invariant penalty term for the maximum penalized likelihood estimator within the empirical Bayes framework, see our article on Objective Priors in the Empirical Bayes Framework.