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An Information-Theoretic Empirical Bayes Method and its Application to a Systems Biology Model

Please always quote using this URN: urn:nbn:de:0297-zib-65098
  • 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.
Author:Alexander SikorskiORCiD
Document Type:Master's Thesis
Granting Institution:Freie Universität Berlin
Advisor:Susanna Röblitz
Date of final exam:2017/03/20
Year of first publication:2017
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