The application of probability theory / edited by Olga Moreira.

Includes bibliographical references and index.

Introduction -- Missing data approaches for probability regression models with missing outcomes with applications -- Maximum likelihood estimation for three-parameter weibull distribution using evolutionary strategy -- Probability distribution and deviation information fusion driven support vector regression model and its application -- Cascade source inference in networks: a markov chain Monte Carlo approach -- PICF-LDA: a topic enhanced LDA with probability incremental correction factor for web API service clustering -- The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments -- Comparison of neural network and logistic regression analysis to predict the probability of urinary tract infection caused by cystoscopy -- Statistical analysis of orthographic and phonemic language corpus for word-based and phoneme-based polish language modelling -- Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches -- Comparison between the Hamiltonian Monte Carlo method and the Mteropolis-Hastings method for coseismic fault model estimation -- Sequential Monte Carlo method toward online RUL assessment with applications -- Probabilistic forecasting of traffic using multikernel based extreme learning machine -- Value-at-risk ambiguity aversion -- DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction.

"Probability theory is an essential branch of mathematics that provides a robust framework for comprehending and quantifying uncertainty and randomness across diverse domains. Its significance spans a wide range of fields, from statistics and data analysis to machine learning and artificial intelligence, medical and health sciences, natural language processing and information retrieval, and engineering and reliability analysis. In the pages of this book, we embark on a comprehensive exploration of the practical applications of probability theory in these various disciplines, delving into the intricacies of both frequentist and Bayesian perspectives. Our journey commences by thoroughly examining the core principles and concepts that underpin probability theory. We meticulously unravel the intricacies of sample space, events, probability distribution, random variables, probability laws and rules, and expected value. By establishing a solid understanding of these foundational elements, readers will be equipped to comprehend the broader applications of probability theory across different fields"-- Back cover.