Deep Gaussian Process Priors for Bayesian Inverse Problems

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• Aretha Teckentrup (University of Edinburgh)
• Thursday 12 April 2018, 11:30-12:00
• Seminar Room 1, Newton Institute.

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

UNQW04 - UQ for inverse problems in complex systems

Co-authors: Matt Dunlop (Caltech), Mark Girolami (Imperial College), Andrew Stuart (Caltech)

Deep Gaussian processes have received a great deal of attention in the last couple of years, due to their ability to model very complex behaviour. In this talk, we present a general framework for constructing deep Gaussian processes, and provide a mathematical argument for why the depth of the processes is in most cases finite. We also present some numerical experiments, where deep Gaussian processes have been employed as prior distributions in Bayesian inverse problems.

Related Links

• https://arxiv.org/abs/1711.11280 - Preprint

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

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