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Variational inference for partially observed diffusion processes

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If you have a question about this talk, please contact Zoubin Ghahramani.

Variational inference is an approximate inference scheme popular in Machine Learning. In contrast to MCMC it leads to a deterministic solution, is relatively quick to converge and its convergence can easily be checked by monitoring the evolution of the variational bound. In this talk we will recall the basic concepts underlying variational EM and show how this framework can be extended to continuous-time stochastic processes. More specifically, we will apply the variational approach to partially observed diffusion processes and discuss parameter inference in this context. I will also discuss the links with statistical linearisation and sigma-point transformations.

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

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