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Exploring ODE model uncertainty via diffusions, with application to physiological processes

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If you have a question about this talk, please contact Rachel Fogg .

Ordinary differential equations models are extensively used to describe various continuous time phenomena. Although successful in capturing certain aspects of the underlying mechanism, the need for further refinement is becoming increasing apparent. In many applications, including our motivating example from physiological processes, this may be attributed to the inherent system noise. In our approach the system noise is explicitly modelled and disentangled from other sources of error. This is achieved by incorporating a diffusive component while retaining the mean infinitesimal behavior of the system. In PK/PD applications, the problem of inference on the non linear diffusion parameters is further complicated by the presence of measurement error, individual variability, imbalanced designs and so forth. We present a general inference framework through data augmentation which we illustrate through simulated and real PK/PD data.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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