An Introduction to Applied Bayesian Methods
-
- Hardcover
- Taschenbuch
- eBook ausgewählt
-
Form:Einzelkauf Download
-
Sprache:Englisch
76,10 €
inkl. gesetzl. MwSt.Artikel erhalten
Beschreibung
Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
18.11.2026
Verlag
Taylor & Francis eBooksSeitenzahl
240 (Printausgabe)
Dateigröße
14913 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781040459195
An Introduction to Applied Bayesian Methods is a concise yet comprehensive guide designed to help readers master the fundamentals of Bayesian statistical modeling. This book bridges the gap between theory and application, offering practical insights into Bayesian methods through real-world examples and hands-on coding exercises. Covering topics such as regression, hierarchical models, and meta-analysis, it equips readers with the tools to implement Bayesian approaches using R and Stan, making it an essential resource for modern data analysis.
As data complexity grows, traditional frequentist approaches often fall short in flexibility and interpretability. This book, on the other hand, provides a probabilistically consistent framework that adapts seamlessly to complex problems. It emphasizes practical application, showing how Bayesian models can handle variability, uncertainty, and predictive challenges in ways that are both intuitive and robust. Whether you're analyzing textbook prices, soil moisture, or multivariate data, this book demonstrates the power of Bayesian thinking. Supplemental Nimble code is also available online, offering additional flexibility for readers.
This book is ideal for advanced undergraduate students, researchers, and professionals in statistics and related fields. If you have a basic understanding of Bayesian principles and want to deepen your knowledge with practical examples, this book is for you. It's also a valuable resource for educators teaching applied Bayesian methods.
Key Features:
- Comprehensive coverage of Bayesian regression and hierarchical models.
- Practical examples using R and Stan code.
- Step-by-step guidance on model comparison and predictive analysis.
- Includes detailed visual representations for interpreting complex data.
- Clear explanations of posterior distributions and uncertainty visualization.
- Accessible for both beginners and experienced practitioners.
Noch keine Bewertungen vorhanden
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.
Kurze Frage zu unserer Seite
Vielen Dank für Ihr Feedback
Wir nutzen Ihr Feedback, um unsere Produktseiten zu verbessern. Bitte haben Sie Verständnis, dass wir Ihnen keine Rückmeldung geben können. Falls Sie Kontakt mit uns aufnehmen möchten, können Sie sich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice