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 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.
This is a PyMCon 2020 talk
Learn more about PyMCon!
PyMCon is an asynchronous-first virtual conference for the Bayesian community.
We have posted all the talks here in Discourse on October 24th, one week before the live PyMCon session for everyone to see and discuss at their own pace.
If you are available on October 31st you can register for the live session here!, but if you are not don’t worry, all the talks are already available here on Discourse (keynotes will be posted after the conference) and you can network here on Discourse and on our Zulip.
We value the participation of each member of the PyMC community and want all attendees to have an enjoyable and fulfilling experience. Accordingly, all attendees are expected to show respect and courtesy to other attendees throughout the conference and at all conference events. Everyone taking part in PyMCon activities must abide by the PyMCon Code of Conduct. You can report any incident through this from.
If you want to support PyMCon and the PyMC community but you can’t attend the live session, consider donating to PyMC
Do you have suggestions to improve PyMCon? We have an anonymous suggestion box waiting for you
Have you enjoyed PyMCon? Please fill our PyMCon attendee survey. It is open to both async PyMCon attendees and people taking part in the live session.