9780367490188 - bayesian workflow von gelman, andrew; vehtari, aki; mcelreath, richard; simpson, daniel; margossian, charles c. (6 Ergebnisse)
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Hardcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 158,75
EUR 7,58 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 3 verfügbar
Zustand: New.
- Hardcover
Anbieter: PBShop.store US, Wood Dale, IL, USAPBShop.store US
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 183,24
Versand gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Hardcover
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes KönigreichPBShop.store UK
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 174,81
EUR 8,90 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Weitere Bilder
- Hardcover
Anbieter: moluna, Greven, Deutschlandmoluna
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 172,62
EUR 48,99 VersandVersand von Deutschland nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. Andrew Gelman is a professor of statistics and political science at Columbia UniversityAki Vehtari is a professor of computer science at Aalto UniversityRichard McElreath is the director of the Max Planck Institute for .
Bayesian Workflow
Gelman, Andrew/ Vehtari, Aki/ Mcelreath, Richard/ Simpson, Daniel/ Margossian, Charles C.
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 229,71
EUR 17,50 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Hardcover. Zustand: Brand New. 544 pages. 10.00x7.00x10.00 inches. In Stock.
- Weitere Bilder
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 238,00
EUR 66,59 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Buch. Zustand: Neu. Neuware - Bayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. Bayesian Workflow explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers…and textbooks. By systematizing the process of Bayesian model development, the book seeks to improve applied analyses and inspire future innovations in theory, methods, and software. It emphasizes the importance of iterative model building, model checking, computational troubleshooting, and simulated-data experimentation, offering a comprehensive perspective on statistical analysis.Through detailed examples and practical guidance, the book bridges the gap between theory and application, empowering practitioners and researchers to navigate the complexities of Bayesian inference. It is not a checklist or cookbook but a flexible framework for understanding and resolving challenges in statistical modeling and decision-making under uncertainty.Features - Covers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understanding - Demonstrates iterative model development and computational problem-solving through real-world case studies - Explores computational challenges, calibration checking, and connections between modeling and computation - Highlights the importance of checking models under diverse conditions to understand their limitations and improve their robustness - Discusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learning - Includes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and Julia This book is designed for practitioners of applied Bayesian statistics, particularly users of probabilistic programming languages such as Stan, as well as developers of methods and software tailored to these users. It also targets researchers in Bayesian theory and methods, offering insights into understudied aspects of statistical workflows. Instructors and students will find adaptable exercises and case studies to enhance their learning experience. Beyond Bayesian inference, the book's principles are relevant to users of non-Bayesian methods, making it a valuable resource for statisticians, data scientists, and machine learning professionals seeking to improve their modeling and decision-making processes.
