  Leave-one-out cross-validation (LOO-CV) is a popular method for estimating
out-of-sample predictive accuracy. However, computing LOO-CV criteria can be
computationally expensive due to the need to fit the model multiple times. In
the Bayesian context, importance sampling provides a possible solution but
classical approaches can easily produce estimators whose asymptotic variance is
infinite, making them potentially unreliable. Here we propose and analyze a
novel mixture estimator to compute Bayesian LOO-CV criteria. Our method retains
the simplicity and computational convenience of classical approaches, while
guaranteeing finite asymptotic variance of the resulting estimators. Both
theoretical and numerical results are provided to illustrate the improved
robustness and efficiency. The computational benefits are particularly
significant in high-dimensional problems, allowing to perform Bayesian LOO-CV
for a broader range of models, and datasets with highly influential
observations. The proposed methodology is easily implementable in standard
probabilistic programming software and has a computational cost roughly
equivalent to fitting the original model once.

Computation;Methodology;Machine Learning