关于
This skill provides a comprehensive foundation for building robust Bayesian models in Python using PyMC 5. It equips developers and data scientists with the necessary patterns for model specification, distribution selection, and advanced sampling techniques like NUTS and ADVI. By integrating ArviZ diagnostic workflows, the skill helps identify and resolve common issues such as divergent transitions through non-centered parameterization, ensuring that statistical inferences are both valid and efficient. It is particularly useful for those migrating from Stan or BUGS, or for anyone implementing the Bayesian workflow described in 'Statistical Rethinking'.
