Bayesian Methods in Machine Learning Applications and Challenges

Abstract

Bayesian methods have emerged as a powerful and flexible framework in machine learning, offering unique advantages such as uncertainty quantification, model interpretability, and the ability to incorporate prior knowledge. This paper provides a comprehensive overview of Bayesian methods, covering their foundational concepts, applications in machine learning models, advantages, and challenges. We begin by introducing the core principles of Bayesian statistics, including Bayes' theorem, prior and posterior distributions, and conjugate priors. We then explore the application of Bayesian methods to various machine learning models, such as Bayesian linear regression, Gaussian processes, and Bayesian networks, highlighting their use in regression, classification, and probabilistic reasoning. The advantages of Bayesian methods, including their ability to handle small sample learning, adapt to online learning scenarios, and provide interpretable models, are discussed in detail. Additionally, we address the challenges associated with Bayesian methods, such as computational complexity, prior selection, and scalability to high-dimensional data. Finally, we outline future research directions, including scalable Bayesian inference, automated prior selection, and Bayesian deep learning. This paper aims to provide a clear and accessible introduction to Bayesian methods for researchers and practitioners, emphasizing their potential to advance the field of machine learning.