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This repository is intended to be a collection reading material, examples, and other educational material related to explaining the functionality and output of the ArviZ python package.\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"## What is Arviz\\n\",\n+    \"<img src=\\\"https://raw.githubusercontent.com/arviz-devs/arviz/master/doc/logo/ArviZ.png\\\" alt=\\\"Arviz\\\" width=\\\"500\\\"/>\\n\",\n+    \"\\n\",\n+    \"ArviZ is is a Python package for exploratory analysis of Bayesian models. In particular it takes the various outputs from Bayesian computational libraries, such as stan, PyMC3, and others, and provides a common method to plot and understand the results.\\n\",\n+    \"\\n\",\n+    \"More information about how to use ArviZ can be found in the docs. https://arviz-devs.github.io/arviz/index.html\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"## Intended Audience\\n\",\n+    \"While ArviZ strives to provide an easy way for Bayesian practioners to create exploratory plots and statistics, the package and docs alone do not explain how to **use** the exploratory plots and statistics. This repository is aimed to fill that gap, and serve as more in depth explanaton to all the functionality in ArviZ. 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