<![CDATA[Uncertainty quantification is essential in regression tasks where predictions inform high-stakes decisions. We present a practical framework for Bayesian deep nonparametric regression that moves beyond point estimates to deliver calibrated predictive intervals and uncertainty decomposition. The approach employs a heteroscedastic Bayesian neural network trained via Monte Carlo Dropout, enabling the estimation of both epistemic and aleatoric uncertainties without costly Markov chain Monte Carlo sampling. We evaluate the method on a synthetic heteroscedastic regression problem, demonstrating accurate predictive means, well-calibrated 90% prediction intervals, and computational efficiency on CPU-only hardware. The results highlight the method’s suitability for uncertainty-aware regression in resource-constrained settings, and all code is released for reproducibility.]]>
Copyrights © 2025
