Bayesian Workflow - Daniel Simpson, Aki Vehtari, Andrew Gelman, Yuling Yao, Paul-Christian Burkner, Martin Modrak, Lauren Kennedy, Jonah Gabry, Charle

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
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