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SDE sampling algorithms for bounded domains, constraints and noisy gradients

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DDEW03 - Computational Challenges and Emerging Tools

I will discuss sampling procedures based on discretized stochastic differential equations for a wide range of applications in physical modelling and data science. In this talk I will cover accurate weak approximation methods for domains with boundary and constraint manifolds. I will also discuss the problem of sampling systems driven by noisy gradients, as arise in many Bayesian inversion problems.

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

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