The Hotelling Lectures are an annual event in the Department of Statistics & Operations Research at the University of North Carolina – Chapel Hill, honoring the memory of Professor Harold Hotelling (first chairman of the “Department of Mathematical Statistics”, as it was originally named at the time of its inception in 1946).   A distinguished guest speaker presents a series of talks (which are open to the public) and remains in residence at the Department for several days.  The inaugural Hotelling Lectures were given by David R. Cox in 1980, followed in subsequent years by these other distinguished speakers:  Herman Chernoff, Ole Barndorff-Nielsen, Frank Hampel, David Brillinger, David Kendall, Persi Diaconis, Pal Revesz, Willem van Zwet, C.R. Rao, Bradley Efron, Lucien LeCam, Peter Bickel, Ulf Grenander, Larry Shepp, David Donoho, David Siegmund, Herbert Robbins, Lawrence D. Brown, Nancy Reid, S.R.S. Varadhan, Stuart Geman, Iain Johnstone, Peter Hall, Ruth J. Williams, Terry Speed, Thomas Kurtz, Peter McCullagh, Richard Davis, Yuval Peres and Dimitris Bertsimas.

 

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March 2017

Hotelling Lectures: Aad van der Vaart, Leiden University

March 27 @ 3:30 pm - 4:30 pm

Nonparametric Bayesian methods: frequentist analysis Aad van der Vaart Leiden University We present an overview of Bayesian methods to estimate functions or high-dimensional parameter vectors, and discuss the validity (or not) of these methods from a non-Bayesian point of view. For instance, we consider using a Gaussian process as a prior for a regression function or (after exponentiation and normalisation) for a density function. We characterise the rate at which the corresponding posterior distribution can recover a true function as…

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Hotelling Lectures: Aad van der Vaart, Leiden University

March 29 @ 3:30 pm - 5:00 pm

Nonparametric Bayesian methods: frequentist analysis Aad van der Vaart Leiden University A more detailed view of Bayesian methods to estimate functions or high-dimensional parameter vectors, and discuss the validity (or not) of these methods from a non-Bayesian point of view. For instance, we consider using a Gaussian process as a prior for a regression function or (after exponentiation and normalisation) for a density function. We characterise the rate at which the corresponding posterior distribution can recover a true function as…

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