Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models

Cross-validation can be used to measure a model’s predictive accuracy for the purpose of model comparison, averaging, or selection. Standard leave-one-out cross-validation (LOO-CV) requires that the observation model can be factorized into simple terms, but a lot of important models in temporal and spatial statistics do not have this property or are inefficient or unstable when forced into a factorized form. We derive how to efficiently compute and validate both exact and approximate LOO-CV for any Bayesian non-factorized model with a multivariate normal or Student- $$t$$ t distribution on the outcome values. We demonstrate the method using lagged simultaneously autoregressive (SAR) models as a case study.