Keynote: These are a few of my favorite inference diagnostics by Aki Vehtari

Talk Abstract

I discuss some old and some more recent inference diagnostics methods for Markov chain Monte Carlo, importance sampling, and variational inference. When the convergence fails, I simply remember my favorite inference diagnostics, and then I don’t feel so bad.


Aki Vehtari

Aki Vehtari Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland.

His numerous research interests are Bayesian probability theory and methodology, especially probabilistic programming, inference methods, model assessment and selection, non-parametric models such as Gaussian processes, dynamic models, and hierarchical models.

Aki is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and the brand new « Regression and other stories ». He is also a core-developer of the seminal probabilistic programming framework Stan. An enthusiast of open-source software, Aki has been involved in many free software projects such as GPstuff for Gaussian processes and ELFI for likelihood inference.

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