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Piecewise Deterministic Monte Carlo for latent variable models : a case study

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SSD - Stochastic systems for anomalous diffusion

Piecewise Deterministic Monte Carlo (PDMC) methods have been around for some time now and our theoretical understanding has already grown significantly in the last decade. However the application of these methods could do with a boost; unfortunately this presents serious challenges in real-world applications. One of the attractive properties of PDMC is the ability to use unbiased gradients of the target distribution. The applications of this property goes beyond subsampling of data. In this talk we will discuss recent efforts to apply this properties in latent variable models. As we will see, this problem is not completely solved yet, but its study leads to interesting insights.

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

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