A Bayesian approach to parameter identification in Turing systems

Add to your list(s) Download to your calendar using vCal

• Campillo-Funollet, E (University of Sussex)
• Thursday 26 November 2015, 11:00-12:30
• Seminar Room 2, Newton Institute Gatehouse.

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

Coupling Geometric PDEs with Physics for Cell Morphology, Motility and Pattern Formation

We present a methodology to identify parameters in Turing systems from noisy data. The Bayesian framework provides a rigorous interpretation of the prior knowledge and the noise, resulting in an approximation of the full probability distribution for the parameters, given the data. Although the numerical approximation of the full probability distribution is computationally expensive, parallelised algorithms produce good approximations in a few hours. With the probability distribution at hand, it is straightforward to compute credible regions for the parameters. The methodology is applied to a well-known Turing system: the Schnakenberg system.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Featured lists
• INI info aggregator
• Interested Talks
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 2, Newton Institute Gatehouse
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.