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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…

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…

STOR Colloquium: Hongtu Zhu, MD Anderson

April 3 @ 3:30 pm - 4:30 pm

STOR Colloquium: Philip Ernst, Rice University

April 10 @ 3:30 pm - 4:30 pm

Philip Ernst Rice University   Title: Yule's "Nonsense Correlation" Solved!   Abstract: In this talk, I will discuss how I recently resolved a longstanding open statistical problem. The problem, formulated by the British statistician Udny Yule in 1926, is to mathematically prove Yule's 1926 empirical finding of ``nonsense correlation.”  We solve the problem by analytically determining the second moment of the empirical correlation coefficient of two independent Wiener processes. Using tools from Fredholm integral equation theory, we calculate the second…

STOR Colloquium: Ilse Ipsen, North Carolina State University

April 17 @ 3:30 pm - 4:30 pm

Ilse Ipsen North Carolina State University Randomized Algorithms for Matrix Computations   The emergence of massive data sets, over the past fifteen or so years, has lead to the development of Randomized Numerical Linear Algebra. Fast and accurate randomized matrix algorithms are being designed for applications like machine learning, population genomics, astronomy, nuclear engineering, and optimal experimental design.  

Probability Seminar: Rick Durrett, Duke University

April 20 @ 4:15 pm - 5:15 pm

Rick Durrett Duke University Latent voter model on locally tree-like random graphs In the latent voter model, which models the spread of a technology through a social network, individuals who have just changed their choice have a latent period, which is exponential with rate λ during which they will not buy a new device. We study site and edge versions of this model on random graphs generated by a configuration model in which the degrees d(x) have 3 ≤ d(x)…

Probability Seminar: Wilfrid Kendall, University of Warwick

April 27 @ 4:15 pm - 5:15 pm

Wilfrid Kendall University of Warwick   A Dirichlet Form approach to MCMC Optimal Scaling (Joint with Giacomo Zanella and Mylene Bédard) In this talk I will discuss the use of Dirichlet forms to deliver proofs of optimal scaling results for Markov chain Monte Carlo algorithms (specifically, Metropolis-Hastings random walk samplers) under regularity conditions which are substantially weaker than those required by the original approach (based on the use of infinitesimal generators). The Dirichlet form method has the added advantage of…

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