arviz-devs/arviz

Exploratory analysis of Bayesian models with Python

What it solves

ArviZ provides a standardized way to perform exploratory analysis on Bayesian models. It simplifies the process of understanding, checking, and comparing the results of these models, which often involve complex posterior distributions that are difficult to analyze manually.

How it works

It is a Python package that offers a suite of tools for posterior analysis, data storage, model checking, comparison, and diagnostics. It allows users to visualize and analyze the outputs of Bayesian models to ensure they are performing correctly and are providing reliable results.

Who it’s for

It is designed for Bayesian modelers, ranging from beginners who are learning the basics to experienced practitioners who need robust tools for model diagnostics and comparison.

Highlights

  • Posterior analysis and data storage tools
  • Model checking and comparison capabilities
  • Diagnostic functions to ensure model reliability
  • Available as a Python package with a Julia wrapper (ArviZ.jl)

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