Bayesian Workflow (Paperback)

Lingua: inglese

Editore: Taylor & Francis Ltd, 2026

0367490145 / 9780367490140

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Venditore AbeBooks dal 22 giugno 2007

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Paperback. 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.FeaturesCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understandingDemonstrates iterative model development and computational problem-solving through real-world case studiesExplores computational challenges, calibration checking, and connections between modeling and computationHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustnessDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learningIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and JuliaThis 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 books 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9780367490140

Titolo
Bayesian Workflow (Paperback)
Autore
Andrew Gelman
Editore
Taylor & Francis Ltd
Anno di pubblicazione
2026
Condizione
new
Rilegatura
Paperback
Lingua
inglese
ISBN 10
0367490145
ISBN 13
9780367490140

AussieBookSeller

Truganina, VIC, Australia

Venditore con 5 stelle

Venditore AbeBooks dal 22 giugno 2007

Tariffe di spedizione da Australia a U.S.A.

ArticoloDa 25 a 45 giorni lavorativiDa 8 a 14 giorni lavorativi
Primo articoloEUR 31,90EUR 37,93
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