Van Dantzig Seminar

nationwide series of lectures in statistics

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Next Van Dantzig Seminar: April 30, 2021

Speakers: Ryan Martin, Aaron Smith

Programme: (click names or scroll down for titles and abstracts)

15:00 - 16:00 Ryan Martin (North Carolina State University)
16:00 - 16:15 Break
16:15 - 17:15 Aaron Smith (University of Ottawa)
The seminar will be online.
Zoom link:
Meeting ID: 978 1416 7086
Passcode: 863873

Titles and abstracts

  • Ryan Martin

    Imprecise probability and valid statistical inference

    Statistics aims to provide reliable or valid data-driven uncertainty quantification about unknowns. The two dominant schools of thought on this are frequentist and Bayesian, but the dichotomy creates confusion and masks opportunities for new developments. My perspective is that these are opposite ends of a spectrum indexed by the degree of precision in one's prior specification: Bayesian is completely precise, frequentist is vacuous or completely imprecise. To formalize this, I will make use of what I call an inferential model (IM), a generalization of Bayes, fiducial, etc., that allows for imprecision. First, I'll show that even a basic notion of validity cannot be achieved with a precise IM, so frequentist inference must correspond to an imprecise IM. Second, I'll show that a simple kind of imprecise IM, called a "possibility measure", can achieve validity and, moreover, that all frequentist procedures can be characterized by a possibility measure. Finally, I'll discuss practical implications of this characterization, open problems, and new directions. This talk is largely based on work presented in the paper available here:

  • Aaron Smith

    Free Lunches and Approximate Markov chain Monte Carlo

    It is widely known that the performance of MCMC algorithms can degrade quite quickly when targeting computationally expensive posterior distributions, including the posteriors associated with any large dataset. This has motivated the search for MCMC variants that scale well for large datasets. One general approach, taken by several research groups, has been to look at only a subsample of the data at every step. In this talk, we'll discuss some basic "no-free-lunch" results that sometimes provide limits on the performance of many such algorithms. We'll then apply these generic results to realistic statistical problems and proposed algorithms. Finally, I'll discuss some of the many examples that can avoid our generic results; some of these seem to provide a free (or at least cheap) lunch, while others are (to my knowledge) open problems. (Based primarily on work with James Johndrow and Natesh Pillai, as well as Patrick Conrad, Andrew Davis, Youssef Marzouk, Tanya Schmah, Pengfei Wang and Aimeric Zoungrana.)

Van Dantzig Seminar

The Van Dantzig seminar is a nationwide series of lectures in statistics, which features renowned international and local speakers, from the full width of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the "Theory of Collective Phenomena" (i.e. statistics) in Amsterdam. The seminar will convene 4 to 6 times a year at varying locations, and is supported financially by among others the STAR cluster and the Section Mathematical Statistics of the VVS-OR.

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