Calibrated Model Criticism Using Split Predictive Checks
通过分割预测检验实现校准的模型诊断
Assessing how well a Bayesian model generalizes to unobserved data is essential, yet existing general-purpose model checks are either not properly calibrated (as in posterior predictive checks) or fail to be sufficiently general for practical use (e.g., due to requiring model-specific derivations). We propose split predictive checks (SPCs) as a simple, general-purpose class of predictive checks that maintain the usability of posterior predictive checks while directly…