Enhancing Reliability in Machine Learning Models through Bayesian Uncertainty Quantification

Abstract

Machine learning (ML) models are increasingly deployed in domains such as healthcare, finance, autonomous systems, and engineering, where decisions carry significant consequences. While these models achieve high predictive accuracy, their reliability is often compromised by a lack of mechanisms to quantify uncertainty. Deterministic outputs can be misleading, particularly in high-stakes scenarios, where overconfidence in incorrect predictions may lead to severe risks. Uncertainty quantification (UQ) offers a critical solution by enabling models to express both aleatoric uncertainty, which arises from inherent data variability, and epistemic uncertainty, which reflects limited model knowledge. Bayesian statistics provides a principled framework for addressing this challenge by modeling probability distributions over parameters and predictions, thereby enhancing interpretability and trust. This paper examines the foundations of Bayesian UQ, reviews key methods such as Monte Carlo sampling, variational inference, Gaussian processes, and Bayesian neural networks, and explores their application across multiple domains. The discussion highlights how Bayesian UQ improves decision-making, supports transparency, and aligns with ethical and regulatory standards. Despite challenges such as computational cost and prior specification, advances in scalable Bayesian methods and approximate inference are making UQ increasingly practical. By embedding Bayesian reasoning into ML workflows, reliability, safety, and accountability are strengthened, positioning Bayesian UQ as a cornerstone for responsible and trustworthy artificial intelligence.