Bayesian Workflow

By Andrew Gelman, Aki Vehtari, Daniel P. Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Burkner, Martin Modr'ak. 2020

344 citations recorded by the source corpus.

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.

Subfields: Artificial Intelligence, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/29a753c485668451ff9452d506bd4d32b75d60c6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Aki Vehtari
2. Yuling Yao
3. Andrew Gelman
4. Thomas V. Wiecki
5. Osvaldo A. Martin
6. Colin Carroll
7. Paul-Christian Burkner
8. Jonah Gabry

In Information Systems and Management

1. Andrew Gelman
2. Aki Vehtari

Citations in the corpus

• PyMC: a modern, and comprehensive probabilistic programming framework in Python (2023, methodology): “Additionally, PPLs facilitate an iterative modeling process that is now more relevant than ever (Gelman et al., 2020; Martin, Kumar & Lao, 2021).”
• Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors (2020, methodology): “3 Stacking as Part of Bayesian Workflow We view stacking of parallel chains as sitting on the boundary between black-box inference and a larger Bayesian workflow (Gelman et al., 2020).”