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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.
Acquisto del libro
Bayesian Data Analysis, AA.VV.
- Lingua
- Pubblicato
- 2013
- product-detail.submit-box.info.binding
- (Copertina rigida)
Metodi di pagamento
Qui potrebbe esserci la tua recensione.
- Titolo
- Bayesian Data Analysis
- Lingua
- Inglese
- Autori
- AA.VV.
- Editore
- CRC Press
- Pubblicato
- 2013
- Formato
- Copertina rigida
- ISBN10
- 1439840954
- ISBN13
- 9781439840955
- Serie
- Tag
- Saggistica, Libri di testo, Tecnologia & Ingegneria, Manuali e guide, Computer & Internet, Scienza, Tecnologia, Libri di testo di matematica
- Valutazione
- 4,35 su 5
- Descrizione
- 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.