کاربرد نظریه احتمال

"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. Cover HalfTitle Page Title Page Copyright Declaration About the Editor Table of Contents List of Contributors List of Abbreviations Preface Chapter 1: Introduction References Chapter 2: Missing Data Approaches for Probability Regression Models with Missing Outcomes with Applications Abstract Introduction Missing Data Approaches Method Comparisons And Asymptotic Results Poisson Regression Using The Automated Data With Missing Outcomes Estimation Using The Automated Data A Simulation Study An Application Conclusions Acknowledgements References Chapter 3: Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy Abstract Introduction Maximum Likelihood Estimation for Three-parameter Weibull Distribution Evolution Optimization Results and Discussion Conclusions Acknowledgments References Chapter 4: Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and its Application Abstract Introduction Review of SVR Probability Distribution Information Weighted Support Vector Regression Experimental Results Conclusion References Chapter 5: Cascade Source Inference in Networks: a Markov Chain Monte Carlo Approach Abstract Introduction Problem Formulation Source Inference Algorithm Experimental Results Conclusion Acknowledgements References Chapter 6: PICF-LDA: A Topic Enhanced LDA with Probability Incremental Correction Factor for Web API Service Clustering Abstract Introduce Related Work Topic Contribution Degree Experiment Conclusions Acknowledgements References Chapter 7: The Development of a Stochastic Mathematical Model of Alzheimer’s Disease to Help Improve the Design of Clinical Trial Abstract Introduction Material and Methods Results Discussion Acknowledgments References Chapter 8: Comparison of Neural Network and Logistic Regression Analysis to Predict the Probability of Urinary Tract Infection Ca Abstract Introduction Materials and Methods Results Discussion Conclusion References Chapter 9: Statistical Analysis of Orthographic and Phonemic Language Corpus for Word-Based and Phoneme-Based Polish Language Mod Abstract Introduction Orthographic Language Corpus Phonemic Language Corpus Analysis of the Obtained Results and Discussion Example of Practical Application of the Obtained Results for Language Modelling Conclusions Acknowledgements References Chapter 10: Detection of Fake News and Hate Speech for Ethiopian Languages: A Systematic Review of the Approaches Abstract Introduction Related Works Results and Discussion Conclusion and Recommendation References Chapter 11: Comparison between the Hamiltonian Monte Carlo Method and the Metropolis-Hastings Method for Coseismic Fault Model Est Abstract Introduction Method Results Discussion Conclusions Acknowledgements References Chapter 12: Sequential Monte Carlo Method Toward Online RUL Assessment with Applications Abstract Introduction From MCMC To SMC Rul Online Assessment from Performance Degradation An Numerical Example of Cutter Lifetime Assessment Conclusions References Chapter 13: Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine Abstract Introduction Literature Review Methodology PIS Model Construction by Qpso-kelm Application Studies Conclusions Acknowledgments References Chapter 14: Value-at-Risk under Ambiguity Aversion Abstract Background Modeling Normal Distributions Under Ambiguity Value-at-risk and Expected Shortfall Under Ambiguity Aversion Risk Aggregation Conclusion Acknowledgements References Chapter 15: DAViS: A Unified Solution for Data Collection, Analyzation, and Visualization in Real-Time Stock Market Prediction Abstract Introduction Related Literature Preliminary The Proposed Davis Framework Experimental Setup Experimental Result Acknowledgements References Index Back Cover