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ArviZ: a modular and flexible library for exploratory analysis of Bayesian models
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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6
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Journal of Open Source Software, pp. 1-6
Abstract
When working with Bayesian models, a range of related tasks must be addressed beyond inference itself. These include diagnosing the quality of Markov chain Monte Carlo (MCMC) samples, model criticism, model comparison, etc. We collectively refer to these activities as exploratory analysis of Bayesian models. In this work, we present a redesigned version of ArviZ, a Python package for exploratory analysis of Bayesian models (EABM). The redesign emphasizes greater user control and modularity. This redesign delivers a more flexible and efficient toolkit for exploratory analysis of Bayesian models. With its renewed focus on modularity and usability, ArviZ is well-positioned to remain an essential tool for Bayesian modelers in both research and applied settings.
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Martin, O A, Abril-Pla, O, Deklerk, J, Axen, S D, Carroll, C, Hartikainen, A & Vehtari, A 2026, 'ArviZ: a modular and flexible library for exploratory analysis of Bayesian models', Journal of Open Source Software, pp. 1-6. https://doi.org/10.21105/joss.09889