TY - THES A1 - Sikorski, Alexander T1 - An Information-Theoretic Empirical Bayes Method and its Application to a Systems Biology Model N2 - This thesis covers the development and application of an empirical Bayes method to the problem of parameter estimation in systems biology. The goal was to provide a general and practical solution to the Bayesian inverse problem in the case of high dimensional parameter spaces making use of present cohort-data. We show that the maximum penalized likelihood estimator (MPLE) with information penalty is based on natural, information-theoretic considerations and admits the desirable property of transformation invariance. Due to its concavity, the objective function is computationally feasible and its mesh-free Monte-Carlo approximation enables its application to high-dimensional problems eluding the curse of dimensionality. We furthermore show how to apply the developed methods to a real world problem by the means of Markov chain Monte-Carlo sampling (MCMC), affirming its proficiency in a practical scenario. Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65098 ER -