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Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability
University of Freiburg, Germany .
University of Freiburg, Germany .
Technical University of Munich, Germany .
Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
2013 (English)In: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 371, no 1984Article in journal (Refereed) Published
Abstract [en]

Increasingly complex applications involve large datasets in combination with nonlinear and high-dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take advantage of both Bayesian and frequentist methods. The elegance of Bayesian methodology is founded in the propagation of information content provided by experimental data and prior assumptions to the posterior probability distribution of model predictions. However, for complex applications, experimental data and prior assumptions potentially constrain the posterior probability distribution insufficiently. In these situations, Bayesian Markov chain Monte Carlo sampling can be infeasible. From a frequentist point of view, insufficient experimental data and prior assumptions can be interpreted as non-identifiability. The profile-likelihood approach offers to detect and to resolve non-identifiability by experimental design iteratively. Therefore, it allows one to better constrain the posterior probability distribution until Markov chain Monte Carlo sampling can be used securely. Using an application from cell biology, we compare both methods and show that a successive application of the two methods facilitates a realistic assessment of uncertainty in model predictions.

Place, publisher, year, edition, pages
Royal Society, The , 2013. Vol. 371, no 1984
Keyword [en]
identifiability, profile likelihood, Bayesian Markov chain Monte Carlo sampling, posterior propriety, propagation of uncertainty, prediction uncertainty
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-88355DOI: 10.1098/rsta.2011.0544ISI: 000312959700006OAI: oai:DiVA.org:liu-88355DiVA: diva2:602898
Note

Funding Agencies|German Federal Ministry of Education and Research|0315766|LungSys|0315415E|FRISYS|0313921|European Union|EU-FP7 HEALTH-F4-2008-223188|Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (CoReNe HMGU)||Excellence Initiative of the German Federal and State Governments|EXC 294|

Available from: 2013-02-04 Created: 2013-02-04 Last updated: 2017-12-06

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Timmer, Jens

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