En savoir plus sur le livre
This book serves three key roles: as an introductory text on Bayesian inference from first principles, a graduate-level guide on current Bayesian modeling and computational approaches, and a practical handbook for applied statistics users and researchers. While the early sections are introductory, the content is not elementary and requires a foundation in basic probability, statistics, elementary calculus, and linear algebra. Chapter 1 provides a review of probability notation and outlines the assumed knowledge. The book emphasizes practical applications, recognizing that readers should have experience in probability, statistics, and linear algebra with a strong computational focus. Merely presenting an introductory text would leave readers lacking guidance for real-world applications, especially where Bayesian methods align with traditional non-Bayesian analyses. Conversely, introducing advanced methods without foundational concepts would be inadequate. The text includes a variety of worked examples from real applications to illustrate current Bayesian methodologies. To maintain clarity, bibliographic notes are provided at the end of each chapter, along with a comprehensive list of references at the conclusion of the book.
Achat du livre
Bayesian Data Analysis, Collectif d'auteurs
- Langue
- Année de publication
- 2013
- product-detail.submit-box.info.binding
- (rigide)
Modes de paiement
Il manque plus que ton avis ici.
- Langue
- Anglais
- Auteurs
- Collectif d'auteurs
- Éditeur
- CRC Press
- Publié
- 2013
- Format
- rigide
- ISBN10
- 1439840954
- ISBN13
- 9781439840955
- Séries
- Mots clés
- Nonfiction, Manuels, Technologie & Ingénierie, Manuels et guides, Informatique & Internet, Science, Technologie, Manuels de mathématiques
- Évaluation
- 4,35 sur 5
- Description
- This book serves three key roles: as an introductory text on Bayesian inference from first principles, a graduate-level guide on current Bayesian modeling and computational approaches, and a practical handbook for applied statistics users and researchers. While the early sections are introductory, the content is not elementary and requires a foundation in basic probability, statistics, elementary calculus, and linear algebra. Chapter 1 provides a review of probability notation and outlines the assumed knowledge. The book emphasizes practical applications, recognizing that readers should have experience in probability, statistics, and linear algebra with a strong computational focus. Merely presenting an introductory text would leave readers lacking guidance for real-world applications, especially where Bayesian methods align with traditional non-Bayesian analyses. Conversely, introducing advanced methods without foundational concepts would be inadequate. The text includes a variety of worked examples from real applications to illustrate current Bayesian methodologies. To maintain clarity, bibliographic notes are provided at the end of each chapter, along with a comprehensive list of references at the conclusion of the book.