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How to think about Bayesian, frequentist, and likelihoodist methods

July 18, 2017 @ 6:00 pm - 8:30 pm

How to think about Bayesian, frequentist, and likelihoodist methods 

Speaker Bio: Greg Gandenberger is a data scientist at Uptake. He has a Ph.D. in History and Philosophy of Science from the University of Pittsburgh.

There are longstanding debates about whether frequentist or Bayesian methods are more appropriate for evaluating the outcomes of A/B tests. According to many accounts, the main point at issue in these debates is whether probabilities are long-run frequencies or degrees of belief. These accounts are not helpful to practitioners, because whether a frequentist or Bayesian approach will work better for a given problem has little if anything to do with the true nature of probability. I provide a different perspective on these debates that keeps the focus on methodological issues: frequentist and Bayesian methods, as well as methods of a third kind called “likelihoodist,” are different responses to the inadequacy of the apparent truism that one should proportion one’s beliefs to one’s evidence.

Warning: This talk addresses issues that are valuable for data practitioners to understand, but it does not contain any code and is not immediately practical.

The following blog posts provide useful background for this talk:




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The event will be livestreamed at here.

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July 18, 2017
6:00 pm - 8:30 pm
Event Category:


Metis Chicago
1033 West Van Buren St, 3rd Floor
Chicago, IL us


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