Connectionists: Bayesian Machine Learning at Scale Webinar

[david rohde]

Laplace's Demon: Bayesian Machine Learning at Scale has a few announcements.



Firstly registration is now open for Jake Hofman 17 June talk: "How
visualizing inferential uncertainty can mislead readers about treatment
effects in scientific results".  Jake is a Senior Principal Researcher at
Microsoft Research, New York.  We very much look forward to his insights on
visualizing uncertainty.  It is at 15.00 UTC, to see it in your local time
zone please go to the registration page.  The talk is on this Wednesday.
Please register at:
https://ailab.criteo.com/laplaces-demon-bayesian-machine-learning-at-scale/



Secondly Christian Robert's talk on approximate Bayesian computation is now
online.  Christian not only presents state of results showing ABC using
Gibbs like steps, but also takes time to give the basis of ABC methods and
takes many questions.  https://www.youtube.com/watch?v=Aq4juvSsz9Y



Finally we have a new website, giving details of upcoming talks including
A/Prof Aki Vehatari's 24 June on "Use of reference models in variable
selection".
https://ailab.criteo.com/laplaces-demon-bayesian-machine-learning-at-scale/



Also upcoming:



17 Jun, Jake Hofman, "How visualizing inferential uncertainty can mislead
readers about treatment effects in scientific results"

24 Jun, Aki Vehtari, "Use of reference models in variable selection"

1 Jul John Ormerod

8 Jul Victor Elvira

29 Jul Cheng Zhang

26 Aug Andrew Gelman



Looking forward to seeing you soon,

The Laplace's Demon Team
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