关于
This skill provides a comprehensive foundation for probabilistic programming within the PyMC 5 ecosystem, enabling developers to build robust Bayesian models with correct syntax, distribution parameterizations, and sampling configurations. It streamlines the entire Bayesian workflow—from defining priors and likelihoods using PyTensor math to performing rigorous diagnostic checks with ArviZ, including prior/posterior predictive checks and model comparisons. It is particularly useful for ensuring statistical best practices, such as identifying when to use non-centered parameterization and interpreting MCMC convergence metrics.
