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Please note: The University of Waterloo is closed for all events until further notice.

Events by month

July 2020

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Thursday, July 16, 2020 — 5:00 PM EDT

Multivariate Extremes: Block-Maxima vs Peak-Over-Threshold” 

Extreme value theory is concerned with describing the tail behaviour of univariate and multivariate distributions. In the estimation of the dependence structure of the extremes of multiple time series, the block maxima method and the peaks-over-threshold method are frequently applied. In this talk, I will compare these methods and propose some new methodologies. This is joint work with A. Bücher and S. Volgushev.

Nan is a lecturer in the Department of Mathematics and Statistics at Macquarie University in Sydney, Australia.

Please note: This seminar will be delivered via Zoom. Please check back later for the link. 

*This seminar will start at 5:00 p.m.

Thursday, July 23, 2020 — 4:00 PM EDT

Applications of Nonstandard Analysis to Markov Processes

Nonstandard analysis, a powerful machinery derived from mathematical logic, has had many applications in probability theory as well as stochastic processes. Nonstandard analysis allows construction of a single object---a hyperfinite probability space---which satisfies all the first order logical properties of a finite probability space, but which can be simultaneously viewed as a measure-theoretical probability space via the Loeb construction. As a consequence, the hyperfinite/measure duality has proven to be particularly in porting discrete results into their continuous settings. 

In this talk, for every general-state-space discrete-time Markov process satisfying appropriate conditions, we construct a hyperfinite Markov process which has all the basic order logical properties of a finite Markov process to represent it.  We show that the mixing time and the hitting time agree with each other up to some multiplicative constants for discrete-time general-state-space reversible Markov processes satisfying certain condition. Finally, we show that our result is applicable to a large class of Gibbs samplers and Metropolis-Hasting algorithms.

Please note: This seminar will be delivered online through Webex. To join, please follow this link: Virtual seminar by Kevin (Haosui) Duanmu.

Wednesday, July 29, 2020 — 4:00 PM EDT

A statistician's introduction to genomics

A classical model of genetic association is introduced alongside a short history of its development with a particular focus on mouse models. The inferential consequences of the widespread use of mouse models are discussed, and the modern application of this model is introduced as a problem of measuring pairwise associations in a large data set. A broad algebraic framework for this model and others like it is used to demonstrate several results and suggest future avenues of investigation.

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