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Seminar on Bayesian Inference (full day event!)

We're pleased to announce that John Myles White will be in town on Tuesday, March 20th, for a full-day seminar on Bayesian Inference at the Computer Science Department of George Mason University in Fairfax, VA. The event is open to the public, and we would be very happy for DSDC Members and others to attend! There is a small charge to cover John's expenses, which is optional for students and faculty of GMU and other educational institutions.

The seminar begins at 8am and ends at 3:30pm, with a break from 11am to 12:30pm. The location is "SUB II (The Hub) Rooms 3, 4, and 5", and The Hub can be found on this map of campus (pdf).

The content to be covered is as follows:

Section 1:

  • An Introduction to Bayesian Inference
  • Introduce the Bayesian paradigm of inference as probabilistic calculation
  • Provide a loose treatment of the Cox axioms
  • Discuss useful statistical theory:
  • Likelihood functions
  • Maximum likelihood estimation
  • Fisher information
  • Bias, variance, consistency and the Central Limit Theorem for estimators
  • Review standard probability distributions
  • Go through the classical coin-filpping example in detail with a beta prior
  • Describe results of Bayesian inference as comparable to MLE with regularization added in

Section 2:

  • BUGS as a Tool for Automating Bayesian Inference
  • Describe how to specify models using BUGS language
  • Go through many example models
  • Normal with unknown mean, known variance
  • Normal with unknown mean, unknown variance
  • Linear regression: unknown coefficients and variance, Normal priors
  • Linear regression with Laplace priors
  • Logistic regression
  • Hierarchical models
  • LDA
  • SNA models

Bio: John Myles White is a Ph.D. student in the Princeton Psychology Department, where he studies how humans make decisions both theoretically and experimentally. Along with the political scientist Drew Conway, he is the author of a book recently published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. John is now working with the statistician Mark Hansen on a book for laypeople about exploratory data analysis. He is also the lead maintainer for several popular R packages, including ProjectTemplate and log4r.

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  • Ross M.

    Boy am I sorry I missed this seminar. Any kind of encore performance in DC area planned?

    September 7, 2012

    • Ross M.

      Well, looks really excellent, and thanks for the generosity of posting your talk notes on GitHub. I like especially the 'soup to nuts' approach you took, rather than racing in to Metropolis-Hasting and Gibbs, BUGS, etc. Very clear. Thank you again.

      September 8, 2012

    • Ross M.

      Thanks, Steve, will check it out!

      September 8, 2012

  • Jerome Y.

    Excellent introduction to Bayesian inference. Clear explanations and interesting, concrete examples.

    March 22, 2012

  • A former member
    A former member

    The practical part has been more my part than the first part. That being said, both parts had been very informative, lively and very good.

    March 22, 2012

  • Nathan O.

    It was a great experience and John did a great job conveying a difficult topic to a number of people with very different backgrounds.

    March 21, 2012

  • Greg B.

    Very interesting topic. I need to do a touch more background reading to take full advantage of the subject matter.

    March 21, 2012

  • LiMing S.

    Very informative.

    March 21, 2012

  • C. S.

    Learned quite a bit from the speaker; would have been nice if the professors in the audience were less intent on trying to show how smart they were...

    March 21, 2012

  • Judy S.


    March 21, 2012

  • Kevin C.

    John did a great job. The long break in between sections was unfortunate but that was the only bad thing I can think of and it wasn't that big a deal.

    March 21, 2012

  • John Myles W.

    For those interested, the slides from yesterday's talk are available at

    March 21, 2012

  • Clarence D.

    John did a nice job and I walked away feeling like this is something that I could (and should) attempt when the project calls for it. I think a one-day seminar is a hard target to hit for a lecturer, especially with such a diverse audience. Thanks!

    March 21, 2012

  • lee de c.

    The instructor has an extremely engaging style of delivery; his incorporation of the many questions into the presentation made it an exciting and dynamic event. the exposition was clear and the examples well thought-out.

    i would have liked a few actual demonstrations of the program...

    March 20, 2012

  • A former member
    A former member

    Being an all day event: around what time do you think it will finish?

    March 12, 2012

  • John Myles W.

    I'm going to assume exposure to statistical theory up to the ideas of IID data and maximum likelihood, but I'll actually cover all of that from scratch during the morning at a fast pace. The second session will be purely computational, so you could manage with almost zero theory, except for the names of the common probability distributions.

    March 6, 2012

  • Sisi W.

    What kind of background in statistics would we need to get the biggest takeaway from this event? I'd love to come, and have a decent grasp of basic statistics, but would the topics being taught go over my head?

    March 5, 2012

Your organizer's refund policy for Seminar on Bayesian Inference (full day event!)

Refunds offered if:

  • the Meetup is cancelled
  • the Meetup is rescheduled
  • you can cancel at least 0 day(s) before the Meetup

Payments you make go to the organizer, not to Meetup. You must make refund requests to the organizer.

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