Fast approximate Bayesian inference for stable differential equation models

Philip Maybank; Ingo Bojak; Richard G. Everitt

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arXiv · 1706.00689

Fast approximate Bayesian inference for stable differential equation models

Philip Maybank·Ingo Bojak·Richard G. Everitt

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Abstract

Inference for mechanistic models is challenging because of nonlinear interactions between model parameters and a lack of identifiability. Here we focus on a specific class of mechanistic models, which we term stable differential equations. The dynamics in these models are approximately linear around a stable fixed point of the system. We exploit this property to develop fast approximate methods for posterior inference. We illustrate our approach using simulated data on a mechanistic neuroscience model with EEG data. More generally, stable differential equation models and the corresponding inference methods are useful for analysis of stationary time-series data. Compared to the existing state-of-the art, our methods are several orders of magnitude faster, and are particularly suited to analysis of long time-series (>10,000 time-points) and models of moderate dimension (10-50 state variables and 10-50 parameters.)

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BibTeX RIS

Philip Maybank, Ingo Bojak, Richard G. Everitt. 2017-06-02. Fast approximate Bayesian inference for stable differential equation models. https://arxiv.org/abs/1706.00689

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