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Bayes’ Theorem in Machine Learning: A Literature Review
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Abstract
In the era of big data and artificial intelligence, machine learning has become a crucial tool for extracting insights and making predictions across various domains. Bayes’ theorem, a fundamental principle in probability theory, has emerged as a cornerstone in many machine learning algorithms. This literature review explores the main applications of Bayes’ theorem in machine learning, focusing on its role in classification, Natural Language Processing (NLP), and other emerging fields. The study aims to provide an overview of how Bayesian principles enhance learning algorithms, improve decision-making processes, and address complex problems in artificial intelligence. Through a systematic review of academic papers from Google Scholar, this research synthesizes current knowledge on Bayesian methods in machine learning. The methodology involves defining the research scope, conducting a literature search using specific keywords, screening relevant studies, and analyzing the collected data. By examining diverse applications ranging from disease prediction to sentiment analysis, this review highlights the versatility and significance of Bayes’ theorem in advancing machine learning techniques and their real-world implementations.
Keywords
Bayes’ theorem, Machine learning, Artificial intelligence
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Cite this article
Qu,B. (2025). Bayes’ Theorem in Machine Learning: A Literature Review. Theoretical and Natural Science,86,26-31.
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