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Empirical Bayes Methods for Prior Estimation in Systems Medicine

Please always quote using this URN: urn:nbn:de:0297-zib-61307
  • One of the main goals of mathematical modelling in systems medicine related to medical applications is to obtain patient-specific parameterizations and model predictions. In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Therefore, before applying Bayes’ rule separately to the data of each patient (which is typically performed using a non-informative prior), it is meaningful to use empirical Bayes methods in order to construct an informative prior from all available data. We compare the performance of four priors - a non-informative prior and priors chosen by nonparametric maximum likelihood estimation (NPMLE), by maximum penalized lilelihood estimation (MPLE) and by doubly-smoothed maximum likelihood estimation (DS-MLE) - by applying them to a low-dimensional parameter estimation problem in a toy model as well as to a high-dimensional ODE model of the human menstrual cycle, which represents a typical example from systems biology modelling.

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Author:Ilja Klebanov, Alexander SikorskiORCiD, Christof Schütte, Susanna RöblitzORCiD
Document Type:ZIB-Report
Tag:Bayesian hierarchical modelling; Bayesian inference; DS-MLE; EM algorithm; Jeffreys prior; MPLE; NPMLE; Parameter estimation; hyperparameter; hyperprior; principle of maximum entropy; reference prior
MSC-Classification:62-XX STATISTICS
Date of first Publication:2016/12/01
Series (Serial Number):ZIB-Report (16-57)
ArXiv Id:http://arxiv.org/abs/1612.01403
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