About:
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. For more information please see the University of Glasgow webpage
Call details will be sent out 30mins before the start of the seminar
These seminars are recorded. All recordings can be found here.
PLEASE NOTE AS OF SEPTEMBER 2021 - THESE SEMINARS WILL BE FORTNIGHTLY.
Future Events:
Probabilistic energy forecasting: successes and challenges
Energy systems are evolving rapidly as they decarbonize, consequences of which include an increasing dependence on weather and new consumer (and producer) behaviours. As a result, all actors in the energy sector are more reliant than ever on short-term forecasts, from the National Grid to me and (maybe) you. Furthermore, in operate as economically as possible and maintain high standards of reliability, forecast uncertainty must be quantified and managed. This seminar will introduce energy forecasting, highlight statistical challenges in this area, and present some recent solutions including forecasting extreme quantiles and modelling time-varying covariance structures.
Past Events:
Advancements of non-Gaussian random fields for statistical inversion
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.