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Scalable MCMC

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If you have a question about this talk, please contact Rowan McAllister.

In the talk we are going to review some recent approaches to scaling up MCMC to large data sets. We will cover approached based on both the Metropolis-Hastings algorithm and Hamiltonian Monte Carlo. Common for all the approached is that they make use of subsampling to cut the computation budget.

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

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