Bayesian Uncertainty Quantification of Differential Equations

Author(s): Oksana Chkrebtii, David Campbell, Mark Girolami, Ben Calderhead
Category: Paper
Notes: We develop a fully Bayesian inferential framework to quantify uncertainty in models defined by general systems of analytically intractable differential equations. This approach provides a statistical alternative to deterministic numerical integration for estimation of complex dynamic systems, and prob- abilistically characterises the solution uncertainty introduced when models are chaotic, ill-conditioned, or contain unmodelled functional uncertainty. Viewing solution estimation as an inference problem allows us to quantify numerical uncertainty using the tools of Bayesian function estimation, which may then be propagated through to uncertainty in the model parameters and subsequent predictions. We incorporate regularity assumptions by modelling system states in a Hilbert space with Gaussian measure, and through iterative model-based sampling we obtain a posterior measure on the space of possible solutions, rather than a single deterministic numerical solution that approximately satisfies model dynamics. We prove some useful properties of this probabilistic solution, propose efficient computational implementation, and demonstrate the methodology on a wide range of challenging forward and inverse problems. Finally, we incorporate the approach into a fully Bayesian framework for state and parameter inference from in- complete observations of the states. Our approach is successfully demonstrated on ordinary and partial differential equation models with chaotic dynamics, ill-conditioned mixed boundary value problems, and an example characterising parameter and state uncertainty in a biochemical signalling pathway which incorporates a nonlinear delay-feedback mechanism.

Back to Directory

Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License