ArviZ a unified library for exploratory analysis of Bayesian models in Python

While conceptually simple, Bayesian methods can be mathematically and numerically challenging. Probabilistic programming languages (PPLs) implement functions to easily build Bayesian models together with efficient automatic inference methods. This helps separate the model building from the inference, allowing practitioners to focus on their specific problems and leaving PPLs to handle the computational details for them (Daniel Roy 2015; Bessiere et al. 2013; Ghahramani 2015). The inference process generates a posterior distribution — which has a central role in Bayesian statistics — together with other distributions like the posterior predictive distribution and the prior predictive distribution. The correct visualization, analysis, and interpretation of these distributions is key to properly answer the questions that motivate the inference process.