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Probabilistic numerics: treating numerical computation as learning, or; it's Bayes all the way down

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If you have a question about this talk, please contact Dr Jes Frellsen.

This talk will introduce the probabilistic numerics framework. Probabilistic numerics interprets numerical procedures (e.g. optimisation, linear algebra, integration) as demanding Bayesian inference. This interpretation allows: uncertainty management at all levels of an algorithm; for the benefits of structure in numerical tasks to be realised, and; for no more costly computation to be allocated to any constituent numerical algorithm than is necessary to achieve our overall goals. The talk will particularly focus on recent work in probabilistic approaches to numerical integration: Bayesian quadrature, a robust alternative to MCMC methods. Applications of the techniques will be demonstrated to domains including astrometry and sensor networks, illustrating the superior wall-clock performance of probabilistic numeric techniques.

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

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