Tag: amortized
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A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift
A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift arXiv:2601.07944v1 Announce Type: new Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex and large-scale predictive problems. The recent success of deep neural networks and foundation models has…
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Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Improving the Accuracy of Amortized Model Comparison with Self-Consistency arXiv:2512.14308v1 Announce Type: new Abstract: Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However, ABI methods are highly sensitive to model misspecification: when observed data fall outside the training distribution (generative scope…
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Towards Trustworthy Amortized Bayesian Model Comparison
Towards Trustworthy Amortized Bayesian Model Comparison arXiv:2508.20614v1 Announce Type: new Abstract: Amortized Bayesian model comparison (BMC) enables fast probabilistic ranking of models via simulation-based training of neural surrogates. However, the reliability of neural surrogates deteriorates when simulation models are misspecified – the very case where model comparison is most needed. Thus, we supplement simulation-based training…