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: TBA
Meeting ID: TBA
Passcode: TBA Titles and abstracts

Titles and abstracts

  • Ryan Martin

    Imprecise probability and valid statistical inference

    Statistical inference aims to quantify uncertainty about unknowns based on data. To formalize this, an inferential model (IM) is a mapping data, etc., to a capacity on the parameter space that assigns data-driven degrees of belief to assertions about the unknowns; this generalizes Bayes, fiducial, and other distribution-based inference approaches. Important questions include: what statistical properties should an IM satisfy, and what do these statistical properties imply about the mathematical structure of its capacity? In this talk, I'll start by defining a "validity" property and its consequences. Then I'll summarize some recent results saying that (a) an IM whose capacity is a precise/additive probability can't be valid, and (b) achieving validity, and frequentist error rate control more generally, is very closely tied to IMs whose capacities are nice imprecise/non-additive probabilities. Illustrations and practical implications of these will be presented. Then I'll conclude with some additional details about the interpretation and possible generalizations of the validity property, and some open questions. This talk is largely based on work presented in

  • 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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