Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability

31.12.2012

Raue A, Kreutz C, Theis FJ, Timmer J

Philos Transact A Math Phys Eng Sci. 2013, 371(1984):20110544

Philos Transact A Math Phys Eng Sci.           online article

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. 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.