Year: 2025
Type: article
Abstract: Bayesian methods, rooted in Bayes' theorem, offer a robust framework for statistical inference and decision-making by integrating prior knowledge with new evidence. This paper examines the theoretical foundations, learning paradigms, and applications of Bayesian methods in fields such as data mining, credit risk assessment, and actuarial science. Parametric and theoretical learning are highlighted as essential methodologies that enhance Bayesian inference’s adaptability and reliability in complex environments. The study highlights recent progress, particularly enhancements in Bayesian network architectures and computational methodologies like Markov Chain Monte Carlo (MCMC) and variational inference, which have broadened the applicability of Bayesian techniques to high-dimensional datasets. However, significant challenges remain, particularly in managing computational complexity and addressing ethical considerations, such as data privacy and fairness, in sensitive domains like healthcare. The paper concludes by advocating for continued innovation to overcome these barriers, focusing on scalable algorithms and interdisciplinary collaboration to ensure ethical and efficient implementations. By addressing these challenges, Bayesian methods can sustain their transformative potential across diverse fields, driving progress in data-driven research and decision-making practices.
Source: Theoretical and Natural Science
Author: Ruibo Song
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408421345