Predictive uncertainty in Gaussian process latent variable models is decomposed into epistemic and aleatoric parts via the law of total variance and estimated with Monte Carlo samples and random Fourier features, but the epistemic estimator drops the squared mean term.
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Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Predictive uncertainty in Gaussian process latent variable models is decomposed into epistemic and aleatoric parts via the law of total variance and estimated with Monte Carlo samples and random Fourier features, but the epistemic estimator drops the squared mean term.