arviz-devs/arviz

Health v2 · maintenance only

Adoption not part of the score

1849 stars · 503 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ArviZ is a Python package for exploratory analysis of Bayesian models, providing posterior analysis, diagnostics, model comparison, plotting, and data storage. It also has a Julia wrapper and a modular ArviZ-verse of subpackages (arviz-base, arviz-stats, arviz-plots).

Use cases

• plot posterior distributions from MCMC samples
• compute R-hat and effective sample size diagnostics
• compare Bayesian models with LOO and WAIC
• run posterior predictive checks
• store and share Bayesian inference results in InferenceData format
• visualize trace plots and forest plots
• summarize MCMC chains from PyMC or Stan

When to choose

• you work with Bayesian models in Python and need diagnostics or plots
• you use PyMC, Stan, NumPyro, or other MCMC tools and want a common analysis layer
• you need standardized storage for inference results
• you want publication-quality Bayesian visualization

When to avoid

• you need to fit models yourself rather than analyze fitted ones
• you want a general-purpose plotting library unrelated to Bayesian statistics
• you need frequentist-only statistical analysis

Facets

library · maturity active

data-visualization data-science analytics benchmarking data-science data-visualization python cross-platform bayesian mcmc posterior-analysis diagnostics model-comparison plots xarray statistics bayesian-inference

Member repositories

For agents

markdown · JSON · MCP: product_card(name="arviz-devs/arviz")