index.qmd

---
title: "Bayesian Workflow book: Website"
date: 2026-02-26
date-modified: today
date-format: iso
format:
  html:
    number-sections: false
lightbox: true
---

## Information

:::: {layout="[ 85, 15 ]"}

::: {#first-column}
Website for the **Bayesian Workflow** book by 
[Andrew Gelman](http://www.stat.columbia.edu/~gelman/), 
[Aki Vehtari](https://users.aalto.fi/ave/),
[Richard McElreath](https://xcelab.net/rm/) with 
Daniel Simpson, 
[Charles C. Margossian](https://charlesm93.github.io/),
[Yuling Yao](https://yulingyao.com/),
[Lauren Kennedy](https://jazzystats.com/),
[Jonah Gabry](https://jgabry.github.io/),
[Paul-Christian Bürkner](https://paulbuerkner.com/),
[Martin Modrák](https://www.martinmodrak.cz/),
[Vianey Leos Barajas](https://www.vleosbarajas.com/).

Published by CRC Press in 2026. Copyright by the authors.

- Buy the book via [publisher's website](https://www.routledge.com/Bayesian-Workflow/Gelman-Vehtari-McElreath-Simpson-Margossian-Yao-Kennedy-Gabry-Burkner-Modrak-Barajas/p/book/9780367490140). Expected publication date in June.

<details><summary>How to cite</summary>

Cite the book:

> Gelman, Vehtari, McElreath, Simpson, Margossian, Yao, Kennedy, Gabry, Bürkner, Modrák, Leos Barajas (2026). *Bayesian Workflow*. Chapman & Hall.

If you want to refer to a case study, cite the book and chapter, e.g.

> blah blah (Gelman et al., 2026, Ch 18 code).

BibTeX entry:

```
@book{Bayesian-Workflow:2026,
  title={Bayesian Workflow},
  author={Andrew Gelman and Aki Vehtari and Richard McElreath and Daniel Simpson and
          Charles C. Margossian and Yuling Yao and Lauren Kennedy and Jonah Gabry and
          Paul-Christian Bürkner and Martin Modrák and Vianey Leos Barajas},
  year=2026,
  publisher={Chapman & Hall}
}
```
</details>
:::

::: {#second-column}
![](9780367490188.png){width=120 fig-alt="Cover of the Bayesian Workflow book"}
:::

::::

## Description

Bayesian statistics and statistical practice have evolved over the
years, driven by advancements in theory, methods, and computational
tools. This book 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.

## Book contents

**Part 1: From Bayesian inference to Bayesian workflow**

1. Bayesian theory and Bayesian practice
2. Statistical modeling and workflow
3. Computational tools
4. Introduction to workflow: Modeling performance on a multiple choice exam

**Part 2: Statistical workflow**

5. Building statistical models
6. Using simulations to capture uncertainty
7. Prediction, generalization, and causal inference
8. Visualizing and checking fitted models
9. Comparing and improving models
10. Statistical inference and scientific inference

**Part 3: Computational workflow**

11. Fitting statistical models
12. Diagnosing and fixing problems with fitting
13. Approximate algorithms and approximate models
14. Simulation-based calibration checking
15. Statistical modeling as software development

**4. Case studies**

16. Coding a series of models: Simulated data of movie ratings
17. Prior specification for regression models: Reanalysis of a sleep study
18. Predictive model checking and comparison: Clinical trial
19. Building up to a hierarchical model: Coronavirus testing
20. Using a fitted model for decision analysis: Mixture model for time series competition
21. Posterior predictive checking: Stochastic learning in dogs
22. Incremental development and testing: Black cat adoptions
23. Debugging a model: World Cup football
24. Leave-one-out cross validation model checking and comparison: Roaches
25. Model building and expansion: Golf putting
26. Model building with latent variables: Markov models for animal movement
27. Model building: Time-series decomposition for birthdays
28. Models for regression coefficients and variable selection: Student grades
29. Sampling problems with latent variables: No vehicles in the park
30. Challenge of multimodality: Differential equation for planetary motion
31. Simulation-based calibration checking in model development workflow

**Appendices**

A. Statistical and computational workflow for Bayesians and non-Bayesians<br>
B. How to get the most out of Bayesian Data Analysis