Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
IEEE

Probabilistic Modeling of Train Operations for Uncertainty Quantification: A Context-Aware Bayesian Network Approach

Abstract: In this study, train operations were modeled by Bayesian networks (BN), to use the probability essence of the BN to quantify their uncertainty (e.g., the epistemic and aleatoric uncertainty ...
IEEE

High-Confidence Classification of Partial Discharge Acoustic Signals Using Bayesian Networks for Uncertainty Quantification

Abstract: Acoustic emission (AE) detection is a nonintrusive technique for monitoring transformer conditions by capturing emissions from partial discharges (PDs), hotspots, and noise. Machine learning ...