The application of probability theory
eBook - PDF

The application of probability theory

  1. 399 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

The application of probability theory

About this book

"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.

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Yes, you can access The application of probability theory by Olga Moreira in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Arcler Press
Year
2024
eBook ISBN
9781774699805
Topic
Mathematics
Subtopic
Probability & Statistics
Index
Mathematics

Table of contents

  1. Cover
  2. HalfTitle Page
  3. Title Page
  4. Copyright
  5. Declaration
  6. About the Editor
  7. Table of Contents
  8. List of Contributors
  9. List of Abbreviations
  10. Preface
  11. Chapter 1: Introduction
  12. Chapter 2: Missing Data Approaches for Probability Regression Models with Missing Outcomes with Applications
  13. Chapter 3: Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy
  14. Chapter 4: Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and its Application
  15. Chapter 5: Cascade Source Inference in Networks: a Markov Chain Monte Carlo Approach
  16. Chapter 6: PICF-LDA: A Topic Enhanced LDA with Probability Incremental Correction Factor for Web API Service Clustering
  17. Chapter 7: The Development of a Stochastic Mathematical Model of Alzheimer’s Disease to Help Improve the Design of Clinical Trial
  18. Chapter 8: Comparison of Neural Network and Logistic Regression Analysis to Predict the Probability of Urinary Tract Infection Ca
  19. Chapter 9: Statistical Analysis of Orthographic and Phonemic Language Corpus for Word-Based and Phoneme-Based Polish Language Mod
  20. Chapter 10: Detection of Fake News and Hate Speech for Ethiopian Languages: A Systematic Review of the Approaches
  21. Chapter 11: Comparison between the Hamiltonian Monte Carlo Method and the Metropolis-Hastings Method for Coseismic Fault Model Est
  22. Chapter 12: Sequential Monte Carlo Method Toward Online RUL Assessment with Applications
  23. Chapter 13: Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine
  24. Chapter 14: Value-at-Risk under Ambiguity Aversion
  25. Chapter 15: DAViS: A Unified Solution for Data Collection, Analyzation, and Visualization in Real-Time Stock Market Prediction
  26. Index
  27. Back Cover