SQUIDS Seminar: Marie-Therese Wolfram - Ensemble Inference Methods for Models with Noisy and Expensive Likelihood
| Dates: | 24 May 2023 |
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| Times: | 12:00 - 13:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Mathematics |
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| Who is it for: | University staff, External researchers, Current University students |
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| Speaker: | Prof. Marie-Therese Wolfram |
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Join us for this research seminar, part of the SQUIDS (Statistics, quantification of uncertainty, inverse problems and data science) seminar series.
Abstract: In this talk we focus on interacting particle systems for the solution of the resulting inverse problems for parameters. Of particular interest is the case where the available forward model evaluations are subject to rapid fluctuations, in parameter space, superimposed on the smoothly varying large scale parametric structure of interest. Multiscale analysis is then used to analyze the behaviour of interacting particle system algorithms when rapid fluctuations, which we refer to as noise, pollute the large scale parametric dependence of the parameter-to-data map. We compare ensemble Kalman methods and Langevin-based methods in this light. The ensemble Kalman methods are shown to behave favourably in the presence of noise in the parameter-to-data map, whereas Langevin methods are adversely affected. On the other hand, Langevin methods have the correct equilibrium distribution in the setting of noise-free forward models, whilst ensemble Kalman methods only provide an uncontrolled approximation, except in the linear case. We therefore introduce a new class of algorithms - so called ensemble Gaussian process samplers - which combine the benefits of both ensemble Kalman and Langevin methods, and perform favourably.
Speaker
Prof. Marie-Therese Wolfram
Organisation: University of Warwick
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Frank Adams 2
Alan Turing Building
Manchester
