<tt>zeus</tt>: a <scp>python</scp> implementation of ensemble slice sampling for efficient Bayesian parameter inference
نویسندگان
چکیده
ABSTRACT We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle computational challenges posed by modern astronomical and cosmological analyses. In particular, requires only minimal hand-tuning 1−2 hyperparameters that are often trivial set; its performance insensitive linear correlations it can scale up 1000s CPUs without any extra effort. Furthermore, locally adaptive nature allows sample efficiently even when strong non-linear present. Lastly, achieves high in strongly multimodal distributions dimensions. Compared emcee, popular MCMC sampler, zeus performs 9 29 times better an exoplanet application, respectively.
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ژورنال
عنوان ژورنال: Monthly Notices of the Royal Astronomical Society
سال: 2021
ISSN: ['0035-8711', '1365-8711', '1365-2966']
DOI: https://doi.org/10.1093/mnras/stab2867