The Application of Probability Theory is a comprehensive book that explores the diverse applications of probability theory across various fields, ranging from statistics and data analysis to machine learning and artificial intelligence, medical and health sciences, natural language processing, information retrieval, and engineering. The book delves into the fundamental principles and concepts of probability theory, such as sample space, events, probability distribution, random variables, probability laws, and expected value, and highlights the distinctions between frequentist and Bayesian approaches. With a collection of contemporaneous articles, it presents cutting-edge research and practical examples that showcase the relevance and impact of probability theory in understanding uncertainty, making predictions, assessing risks, designing experiments, and conducting statistical inference. Whether it's developing statistical models for missing data, enhancing machine learning algorithms with probability information, optimizing clinical trial designs for Alzheimer's disease, predicting urinary tract infections, or detecting fake news and hate speech, this book serves as a valuable resource for researchers, practitioners, and students seeking a deeper understanding of the applications of probability theory in today's rapidly evolving world.