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Accelerated Bayesian inference using deep learning

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I introduce a novel Bayesian inference tool that uses a neural network to parameterise efficient Markov Chain Monte-Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. Neural networks are extremely expressive, and can transform complex targets to a simple latent representation from which one can efficiently sample. Using this method, I develop a nested MCMC sampler, finding excellent performance on highly curved and multi-modal analytic likelihoods. I also demonstrate it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to LCDM in ~20 dimensional parameter space.

This talk is part of the Cavendish Astrophysics Coffee talks series.

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