Virtual Seminar Series - Research and teaching in statistical and data sciences

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International Centre for Mathematical Sciences
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This diverse seminar series will highlight novel advances in methodology and application in statistics and data science, and will take the place of the University of Glasgow Statistics Group seminar during this period of remote working. We welcome all interested attendees at Glasgow and further afield.

To sign up for this seminar series, please complete this form.

The dates of the seminars and speakers are as follows:

23rd April 2020, 10am:

Neil Chada, National University of Singapore

Title: Advancements of non-Gaussian random fields for statistical inversion

Abstract: Developing informative priors for Bayesian inverse problems is an important direction, which can help quantify information on the posterior. In this talk we introduce a new of a class priors for inversion based on $\alpha$-stable sheets, which incorporate multiple known processes such as a Gaussian and Cauchy process. We analyze various convergence properties which is achieved through different representations these sheets can take. Other aspects we wish to address are well-posedness of the inverse problem and finite-dimensional approximations. To complement the analysis we provide some connections with machine learning, which will allow us to use sampling based MCMC schemes. We will conclude the talk with some numerical experiments, highlighting the robustness of the established connection, on various inverse problems arising in regression and PDEs.

7th May 2020, 4pm:

Paul van Dam-Bates, University of St Andrews

Title: Applications of the Halton Sequence for Spatially Balanced Sampling of Natural Resources

Abstract: We will demonstrate by example how some of the properties of the quasi-random Halton sequence can be used for flexible spatially balanced sampling of many different resource types. By introducing Halton boxes we expand Balanced Acceptance Sampling (BAS) for problems such as local sample replacement, double sampling and incorporating legacy sites. We will then show how Halton Iterative Partitioning can be used to select a Halton grid-based sample to create a freshwater master sample that coordinates monitoring at multiple spatial scales. Examples include marine monitoring in Western Canada, terrestrial and stream monitoring in New Zealand and lake monitoring in the Northwest Territories.

14th May 2020, 2pm

Roberta Pappadà, University of Trieste

21st May 2020, 2pm

Ana Basiri, UCL

28 May 2020, time tbd

Nicole Augustin, University of Edinburgh

This seminar series is supported as part of the ICMS/INI Online Mathematical Sciences Seminars.

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