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Probabilistic Numerics

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If you have a question about this talk, please contact Alessandro Davide Ialongo .

Abstract:

After a brief conceptual review of aims and tools, we will cover the Probabilistic Numerical approach to quadrature (integration) and optimisation (quasi-newton methods and stochastic gradient descent).

Reading:

There is no required reading, however Persi Diaconis’ 1988 paper Bayesian Numerical Analysis provides a good, historical introduction to the subject matter of probabilistic numerics.

This talk is part of the Machine Learning Reading Group @ CUED series.

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Sanger Lecture Room, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus Wolfson Lecture Room (AL.06), Centre for Mathematical Sciences Lecture Theatre 6 (LT6), Department of Engineering

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