| Titre : | The application of probability theory |
| Auteurs : | Olga Moreira, Auteur |
| Type de document : | document électronique |
| Editeur : | New York : Academic press, 2024 |
| ISBN/ISSN/EAN : | 978-1-77469-980-5 |
| Format : | 1 vol. (399 p.) / ill. couv. en coul / PDF |
| Note générale : | Bibliogr. |
| Langues: | Anglais |
| Index. décimale : | 519.2 (Probabilités) |
| Catégories : | |
| Mots-clés: | Mathematical statistics Probability |
| Résumé : |
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 |
| Note de contenu : |
Table of Contents: - Chapter 1 Introduction. - Chapter 2 Missing Data Approaches for Probability Regression Models with Missing Outcomes with Applications. - Chapter 3 Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy. - Chapter 4 Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and its Application. - Chapter 5 Cascade Source Inference in Networks: a Markov Chain Monte Carlo Approach. - Chapter 6 PICF-LDA: A Topic Enhanced LDA with Probability Incremental Correction Factor for Web API Service Clustering. - Chapter 7 The Development of a Stochastic Mathematical Model of Alzheimer’s Disease to Help Improve the Design of Clinical Trials of Potential Treatments. - Chapter 8 Comparison of Neural Network and Logistic Regression Analysis to Predict the Probability of Urinary Tract Infection Caused by Cystoscopy. - Chapter 9 Statistical Analysis of Orthographic and Phonemic Language Corpus for Word-Based and Phoneme-Based Polish Language Modelling. - Chapter 10 Detection of Fake News and Hate Speech for Ethiopian Languages: A Systematic Review of the Approaches. - Chapter 11 Comparison between the Hamiltonian Monte Carlo Method and the Metropolis-Hastings Method for Coseismic Fault Model Estimation. - Chapter 12 Sequential Monte Carlo Method Toward Online RUL Assessment with Applications. - Chapter 13 Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine. - Chapter 14 Value-at-Risk under Ambiguity Aversion. - Chapter 15 DAViS: A Unified Solution for Data Collection, Analyzation, and Visualization in Real-Time Stock Market Prediction. |
| Côte titre : | EB/290 |
Exemplaires (1)
| Cote | Support | Localisation | Disponibilité |
|---|---|---|---|
| EB/290 | Ebook | Bibliothèque centrale | Disponible |
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