Self-consistency training on real data improves amortized Bayesian model comparison accuracy under distribution shifts, especially in open-world misspecification when analytic or locally accurate surrogate likelihoods are available.
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
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Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Self-consistency training on real data improves amortized Bayesian model comparison accuracy under distribution shifts, especially in open-world misspecification when analytic or locally accurate surrogate likelihoods are available.