Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Matematika
Iskander, Hasan Mohammed
Unknown Affiliation
Education Mathematics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Beyond Point Estimates: Bayesian Deep Nonparametric Regression with Rigorous Uncertainty Quantification Iskander, Hasan Mohammed
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.2052

Abstract

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.
Co-Authors