November’s AI Fun and ELLIS Invited Speaker is Sam Power from the University of Bristol
Bio: Sam is a Lecturer in the School of Mathematics. Sam received his PhD degree in 2020 from the Statistical Laboratory at the University of Cambridge, where his thesis focused on the synthesis and analysis of stochastic simulation algorithms for Bayesian inference. He then joined the School of Mathematics at Bristol as a postdoctoral research associate, further developing his interests in statistical modelling and computation, before securing a role as Lecturer.
His research interests at present centre around i) theoretical aspects of Markov chain Monte Carlo (MCMC) algorithms, and ii) scalable computational methods for state-space models, with applications to inference in stochastic epidemic models, though he retains broad interests across aspects of applied probability, statistical inference, and computation.
Title: A State-Space Perspective on Modelling and Inference for Online Skill Rating
Abstract: In the quantitative analysis of competitive sports, a fundamental task is to estimate the skills of the different agents (‘players’) involved in a given competition based on the outcome of pairwise comparisons (‘matches’) between said players, often in an online setting. In this talk, I will discuss recent work in which we advocate for adoption of the state-space modelling paradigm in solving this problem. This perspective facilitates the decoupling of modeling from inference, enabling a more focused approach to development and critique of model assumptions, while also fostering the development of general-purpose inference tools.
I will first describe some illustrative model classes which arise in this framework, before turning to a careful discussion of inference and computation strategies for these models. A key challenge throughout is to develop methodology which scales gracefully to problems with a large number of players and a high frequency of matches. I then conclude by describing some real-data applications of our approach, demonstrating how this framework facilitates a practical workflow across different sports.
This is joint work with Samuel Duffield (Normal Computing) and Lorenzo Rimella (Università degli Studi di Torino).
In-person attendance is encouraged but if you can only join online, you are welcome to register via Ticket Source (link provided on this page).
