Publication Date:
2021-02-01
Description:
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.
Language:
English
Type:
masterthesis
,
doc-type:masterThesis
Format:
application/pdf
