Accelerated Bayesian inference using deep learning

Moss, Adam

Abstract

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 https://github.com/adammoss/nnest.

Citation

Moss, A. (2020). Accelerated Bayesian inference using deep learning. Monthly Notices of the Royal Astronomical Society, 496(1), 328-338. https://doi.org/10.1093/mnras/staa1469

• 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
• DOI: https://doi.org/10.1093/mnras/staa1469
• Keywords: Space and Planetary Science; Astronomy and Astrophysics
• Public URL: https://nottingham-repository.worktribe.com/output/4946515
• Publisher URL: https://academic.oup.com/mnras/article-abstract/496/1/328/5848209?redirectedFrom=fulltext
• 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.