ArviZ: a modular and flexible library for exploratory analysis of Bayesian models

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. Statement of needProbabilistic programming has emerged as a powerful paradigm for statistical modeling, accompanied by a growing ecosystem of tools for model specification and inference.Effective modeling requires robust support for uncertainty visualization, sampling diagnostics, model comparison, and model checking (Gelman et al., 2020;Guo et al., 2024;Martin, 2024).ArviZ addresses this gap by providing a unified, backend-agnostic library to perform these tasks.The original ArviZ paper (Kumar et al., 2019) described the landscape of probabilistic programming tools at the time and the need for a unified, backend-agnostic library for exploratory analysis -a need that has only grown as the ecosystem has expanded.The methods implemented in ArviZ are grounded in well-established statistical principles and provide robust, interpretable diagnostics and visualizations (