ArviZ
A Python library for exploratory analysis, diagnostics, and visualization of Bayesian models.
What is ArviZ?
ArviZ is a Python library for exploratory analysis of Bayesian models. It provides tools for posterior analysis, model checking, comparison, and diagnostics, helping users understand and validate their Bayesian inferences. The library supports data storage and visualization, making it easier to work with probabilistic programming outputs.
Data scientists, statisticians, and researchers who use Bayesian modeling and need tools for model diagnostics, visualization, and comparison. It is particularly useful for those working with probabilistic programming languages like PyMC, Stan, or NumPyro.
ArviZ offers a unified and flexible interface for Bayesian model analysis, with extensive visualization capabilities and modular design. It integrates with multiple probabilistic programming backends, providing consistent diagnostics and comparisons across different modeling frameworks.
Overview
Exploratory analysis of Bayesian models with Python
Use Cases
Best For
- Visualizing posterior distributions from MCMC or variational inference
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