Document Detail


Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability.
MedLine Citation:
PMID:  23277602     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Andreas Raue; Clemens Kreutz; Fabian Joachim Theis; Jens Timmer
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-12-31
Journal Detail:
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:
Created Date:  2013-01-01     Completed Date:  2013-03-07     Revised Date:  2013-04-24    
Medline Journal Info:
Nlm Unique ID:  101133385     Medline TA:  Philos Trans A Math Phys Eng Sci     Country:  England    
Other Details:
Languages:  eng     Pagination:  20110544     Citation Subset:  IM    
Affiliation:
Institute for Physics, University of Freiburg, Freiburg, Germany.
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MeSH Terms
Descriptor/Qualifier:
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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