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Webinar: Introduction to Bayesian Data Analysis and Stan

This post is by Eric.

We are starting a series of free webinars about Stan, Bayesian inference, decision theory, and model building. The first webinar will be held on Tuesday, October 25 at 11:00 AM EDT. You can register here.

Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we will demonstrate the use of Stan for some small problems in sports ranking, nonlinear regression, mixture modeling, and decision analysis, to illustrate the general idea that Bayesian data analysis involves model building, model fitting, and model checking. One of our major motivations in building Stan is to efficiently fit complex models to data, and Stan has indeed been used for this purpose in social, biological, and physical sciences, engineering, and business. The purpose of the present webinar is to demonstrate using simple examples how one can directly specify and fit models in Stan and make logical decisions under uncertainty.


Update: a video recording of the webinar is now available here.


  1. L says:

    Will it be posted afterwards? I’d love to watch but I’m not available then.

  2. veblen says:

    Likewise. It’d be great if these are posted somewhere.

    On a somewhat related note, are there (will there be) any resources on teaching probability with Stan?

  3. Howard Edwards says:

    Ditto – that’s 4am my time so a recorded version would be greatly appreciated.

  4. Maza says:

    hi, i never joined a webinar before, is everyone interested free to register? are slots limited ?
    i’m asking because i don’t want to take someone else’s place who may benefit from it more than i will.

  5. Josef Frank says:

    Had the great opportunity to attend yesterday.

    Thanks a lot to Andrew for the inspiring talk
    (and to Eric for hosting the session).

    This indeed was a teaser for using stan
    (rstan in particular), especially the golf example.

    As others I’d also appreciate the recording
    of the session being available for download.

  6. Louis says:

    Agreed, very insightful and entertaining (though Andrew called someone “dumb” or something like that… I chuckled), though I missed part of Stan goes to the worldcup discussion because of colleague dropping by.
    Maybe a future webinar could look at a complex model (something which is indexed in multiple dimensions etc.) and how to build up the model statement in a smart way?

    The webcast worked seamlessly.

  7. Eric Novik says:

    We have some more references from the webinar available here:

    Feel free to leave us comments about what other topics you would like to see in the future.

  8. Gianpaolo Galli says:

    The link is no longer available.
    Can you post a link with the slide in pdf format (bayes_webinar.pdf in the video)?
    Many thanks!

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