uravu: Making Bayesian modelling easy(er)

Abstract

uravu offers an easy to use interface for data analysis using Bayesian modelling in the Python programming language, aiming to make Bayesian modelling as easy to use as the scipy.op timize.curve_fit() method. This software acts to lower the barrier of entry to the use of packages such as: • scipy: for maximum likelihood estimation (Virtanen et al., 2020) • emcee: for Markov chain Monte Carlo investigation of posterior probabilities (Foreman-Mackey et al., 2019) • dynesty: for nested sampling (Skilling, 2006) and dynamic nested sampling (Higson, Handley, Hobson, & Lasenby, 2019) of posterior probabilities and estimation of the Bayesian evidence (Speagle, 2020).