The Bayesian Workflow book is coming!

2026-04-16 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Data Science & Analytics, Mathematics & Computational Sciences · Depth: Advanced, quick

Summary

The new book, "Bayesian Workflow," co-authored by Gelman, Vehtari, McElreath, and others, is now available for pre-order from Routledge and Amazon. This publication, the result of several years of effort, aims to fill gaps left by previous works like "Bayesian Data Analysis" (BDA) by providing comprehensive guidance on practical Bayesian modeling. It covers essential topics such as setting informative priors, troubleshooting computational convergence issues, and designing simulated-data experiments. The book is structured into four parts: an introduction to Bayesian workflow, detailed statistical workflow, computational workflow, and 16 extensive case studies demonstrating various applications, including movie ratings, sleep studies, clinical trials, and coronavirus testing. Appendices also extend workflow concepts to non-Bayesian inference and guide readers on maximizing BDA's value.

Key takeaway

For AI scientists and data practitioners building complex models, this book offers a crucial resource for navigating the practicalities of Bayesian analysis. You should consider integrating its structured workflow and troubleshooting techniques to improve model reliability and interpretability. The detailed case studies provide concrete examples to guide your development of robust statistical and computational processes, especially when dealing with issues like prior specification or convergence.

Key insights

The "Bayesian Workflow" book offers practical guidance for applying Bayesian methods, addressing common challenges in statistical and computational practice.

Principles

• Workflow applies to Bayesian and non-Bayesian inference.
• Simulations are key to capturing uncertainty.
• Statistical modeling is like software development.

Method

The book outlines a workflow for building, fitting, checking, and comparing statistical models, including diagnosing computational problems and using approximate algorithms.

In practice

• Set up informative priors for regression models.
• Debug models using simulation-based calibration.
• Perform leave-one-out cross-validation for model comparison.

Topics

• Bayesian Workflow
• Statistical Modeling
• Computational Workflow
• Prior Specification
• Model Checking

Best for: AI Scientist, Data Scientist, Research Scientist

Related on AIssential

• The stories behind our published research from last year — Gelman's diverse research portfolio emphasizes Bayesian methods, statistical workflow, and critical re-evaluation of scientific practices.
• Jonah’s seminar tomorrow: “Bayesian Workflow and the Software That Shapes It” — Jonah Gabry will present on Bayesian workflow and its software, based on his upcoming book.
• When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systems — A novel wrapper ensures safe, always-valid release decisions for adaptive AI workflows by calibrating black-box scores and accumulating evidence.
• Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials — Scientific data readiness for AI requires a structured, multi-dimensional approach tailored to HPC environments.
• Bayesian Inference: Overview — Bayesian inference updates prior parameter beliefs with data to yield a posterior distribution, balancing evidence and existing knowledge.
• Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions — Context-aware temporal features from IVF lab environments, modeled hierarchically, significantly improve pregnancy rate prediction and are transferable.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.