Joining forces of Bayesian and frequentist methodology: A study for inference in the presence of non-identifiability
Raue A, Kreutz C, Theis FJ, Timmer J (2013)
Publication Type: Journal article
Publication year: 2013
Journal
Book Volume: 371
Article Number: 0544
Journal Issue: 1984
Abstract
Increasingly complex applications involve large datasets in combination with nonlinear and highdimensional 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. © 2012 The Author(s) Published by the Royal Society. All rights reserved.
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Germany (DE)How to cite
APA:
Raue, A., Kreutz, C., Theis, F.J., & Timmer, J. (2013). Joining forces of Bayesian and frequentist methodology: A study for inference in the presence of non-identifiability. Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences, 371(1984). https://dx.doi.org/10.1098/rsta.2011.0544
MLA:
Raue, Andreas, et al. "Joining forces of Bayesian and frequentist methodology: A study for inference in the presence of non-identifiability." Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences 371.1984 (2013).
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