  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 distribution on the outcome values. We
demonstrate the method using lagged simultaneously autoregressive (SAR) models
as a case study.
