Reliability Evaluation

Reliability evaluation assesses the dependability and trustworthiness of systems, models, or predictions across diverse domains, aiming to quantify their performance under uncertainty and potential failures. Current research focuses on developing robust methods for reliability estimation using machine learning models (e.g., neural networks, Bayesian networks), reinforcement learning, and advanced statistical techniques, often incorporating domain-specific knowledge to improve accuracy and efficiency. These advancements are crucial for ensuring the safety and reliability of critical systems in various fields, including healthcare, engineering, and information technology, by providing quantitative measures of performance and identifying potential weaknesses.