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paper/paper.md

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@@ -13,7 +13,7 @@ authors:
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- name: Oriol Abril-Pla
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orcid: 0000-0002-1847-9481
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equal-contrib: true
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corresponding: true
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corresponding: true
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affiliation: 2
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- name: Jordan Deklerk
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affiliation: 3
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# Summary
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`ArviZ` [@Kumar_2019] is a Python package for exploratory analysis of Bayesian models that has been widely used in academia and industry since its introduction in 2019. It's goal is to integrate seamlessly with established probabilistic programming languages and statistical interfaces, such as PyMC [@Abril-pla_2023], Stan (via the cmdstanpy interface) [@stan], Pyro, and NumPyro [@Phan_2019; @Bingham_2019], emcee [@emcee], and Bambi [@Capretto_2022], among others.
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`ArviZ` is part of the broader ArviZ-project, which develops tools for Exploratory Analysis of Bayesian Models. The organization also maintains other initiatives, including arviz.jl (for Julia), PreliZ [@icazatti_2023], educational resources [@eabm_2025], and additional packages that are still in an experimental phase.
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az.ess(dt_samples)
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The only required argument for battery-included plots is the input data, typically a `DataTree` (`dt`), but in the following example we also apply optional customizations.
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az.style.use('arviz-variat')
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dt = az.load_arviz_data("centered_eight")
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pc = az.plot_dist(
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* Essential Open Source Software Round 4 grant by the Chan Zuckerberg Initiative (CZI)
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* ...
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# References
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# References

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