zeus: a PYTHON implementation of ensemble slice sampling for efficient Bayesian parameter inference

Minas Karamanis

doi:10.1093/mnras/stab2867

2021-10-05

Minas Karamanis *

Florian Beutler

J. A. Peacock

DOI：10.1093/mnras/stab2867

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摘要

En 中文

We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle the computational challenges posed by modern astronomical and cosmological analyses. In particular, the method requires only minimal hand-tuning of 1-2 hyperparameters that are often trivial to set; its performance is insensitive to linear correlations and it can scale up to 1000s of CPUs without any extra effort. Furthermore, its locally adaptive nature allows to sample efficiently even when strong non-linear correlations are present. Lastly, the method achieves a high performance even in strongly multimodal distributions in high dimensions. Compared to emcee, a popular MCMC sampler, zeus performs 9 and 29 times better in a cosmological and an exoplanet application, respectively.

Keyword:

methods: data analysis

methods: statistical

techniques: radial velocities

cosmology: large-scale structure of Universe

期刊

Monthly Notices of the Royal Astronomical Society

IF:

4.8

论文数:

7.1W

被引数:

25.0W

机构

University of Edinburgh

学者数:

5.2W

论文数: 4.6W

被引数: 71

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