"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.

lgrsnf/application-probability-theory.rar

Arcler Education Inc

Society Publishing

Delve Publishing

Canada - English Language, Canada

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

2024-08-29