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Accelerated Bayesian inference using deep learning

Moss, Adam


Associate Professor


We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize 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. NNs are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal speed hierarchy, allowing for fast exploration of subspaces where it is inexpensive to evaluate the likelihood. Using this method, we develop a nested MCMC sampler to perform Bayesian inference and model comparison, finding excellent performance on highly curved and multimodal analytic likelihoods. We also test it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to the standard cosmological model in ?20D parameter space. Our method has wide applicability to a range of problems in astronomy and cosmology and is available for download from


Moss, A. (2020). Accelerated Bayesian inference using deep learning. Monthly Notices of the Royal Astronomical Society, 496(1), 328-338.

Journal Article Type Article
Acceptance Date May 19, 2020
Online Publication Date May 28, 2020
Publication Date Jul 21, 2020
Deposit Date Oct 8, 2020
Publicly Available Date Oct 8, 2020
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Oxford University Press (OUP)
Peer Reviewed Peer Reviewed
Volume 496
Issue 1
Pages 328-338
Keywords Space and Planetary Science; Astronomy and Astrophysics
Public URL
Publisher URL
Additional Information This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society©: 2020 The authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.


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