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

详细信息

发表期刊/会议

The Journal of Open Source Software

发表日期

2019-01-15

发表年份

2019

摘要

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 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.