| Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability. | |
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MedLine Citation:
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PMID: 23277602 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Andreas Raue; Clemens Kreutz; Fabian Joachim Theis; Jens Timmer |
Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2012-12-31 |
Journal Detail:
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Title: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Volume: 371 ISSN: 1364-503X ISO Abbreviation: Philos Trans A Math Phys Eng Sci Publication Date: 2013 Feb |
Date Detail:
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Created Date: 2013-01-01 Completed Date: 2013-03-07 Revised Date: 2013-04-24 |
Medline Journal Info:
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Nlm Unique ID: 101133385 Medline TA: Philos Trans A Math Phys Eng Sci Country: England |
Other Details:
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Languages: eng Pagination: 20110544 Citation Subset: IM |
Affiliation:
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Institute for Physics, University of Freiburg, Freiburg, Germany. |
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Bayes Theorem* Computer Simulation Data Interpretation, Statistical* Models, Biological* Models, Statistical* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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