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ArviZ is an open source project aiming to provide tools for Exploratory Analysis of Bayesian Models that do not depend on the inference library used. ArviZ brings together people from several probabilistic programming libraries and from multiple programming languages.
Apr 5, 2024 · ArviZ (pronounced "AR- vees ") is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.
ArviZ is a Python package for exploratory analysis of Bayesian models. It serves as a backend-agnostic tool for diagnosing and visualizing Bayesian inference. Get Started See Gallery
ArviZ is designed to be used with libraries like PyStan and PyMC3, but works fine with raw NumPy arrays. Plotting a dictionary of arrays, ArviZ will interpret each key as the name of a different random variable. Each row of an array is treated as an independent series of draws from the variable, called a chain.
ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.
ArviZ (/ ˈ ɑː r v ɪ z / AR-vees) is a Python package for exploratory analysis of Bayesian models. [2] [3] [4] [5] It is specifically designed to work with the output of probabilistic programming libraries like PyMC , Stan , and others by providing a set of tools for summarizing and visualizing the results of Bayesian inference in a ...
ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate here.
