Abstract: I discuss some parts of Bayesian workflow with focus on the need and justification for iterative workflow. The talk is partly based on a review paper by Gelman, Vehtari, Simpson, Margossian, Carpenter, Yao, Kennedy, Gabry, Bürkner, and Modrák with the following abstract: "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 applied research can involve fitting many models for any given problem, even if only a subset of them are relevant once the analysis is over." The pre-print is available at https://arxiv.org/abs/2011.01808.

Speakers:  Aki Vehtari
My research interests are Bayesian probability theory and methodology,
especially Bayesian workflow, diagnostics, probabilistic programming, inference
methods such as Laplace, EP, VI, MC, model assessment and selection,
non-parametric models such as Gaussian processes, dynamic models, and
hierarchical models. I'm member of Stan and ArviZ development teams. I'm
co-author of text books Bayesian Data Analysis, 3rd ed, and Regression and
Other Stories.

Affiliation: Aalto University

Place of Seminar:  Zoom (Available afterwards Youtube)
Meeting ID: 646 4177 8635
Passcode: 293509