Related events
September 2025
π RSS session on extremes!
Weβre excited to announce that the RSS Conference 2025 will feature an invited session involving extreme value theory. The conference will be held at the Edinburgh International Conference Centre from 1β4 September. Find more details here. See below for information on the sessions.
π Advancing Statistical Frontiers: Extreme Value Theory, Machine Learning, and Point Processes
Day and time (tentative): Tuesday 2nd September 2025, 15:00 - 16:20
Summary
This session explores the intersection of extreme value theory, machine learning, and point processes, showcasing how modern statistical methodologies can address complex, real-world phenomena characterised by rare and extreme events. The session will cover topics such as spatial confounding, preferential sampling, marked point processes, and neural estimation for extremal processes with complex dependencies. This session is designed for statisticians, data scientists, and researchers interested in discovering the potential of blending traditional and machine learning approaches to tackle challenges in spatio-temporal modelling.
Invited speakers
- Ottmar Cronie (department of Mathematical Sciences, Chalmers University of Technology & University of Gothenburg, Sweden). Some new results on marked point processes and their statistical applications.
- Brian Reich (department of Statistics, North Carolina State University). Spatial confounding and preferential sampling
- Julia Walchessen (department of Statistics & Data Science, Carnegie Mellon University). Neural Conditional Simulation for Complex Spatial Processes
Organisers: Daniela Castro-Camilo, Amanda Lenzi and Jorge Mateu.
February 2025
π Jordan talk at RSS Edinburgh Local Group + R-Edinburgh meeting
The first RSS Edinburgh Local Group event of 2025, held in collaboration with the R-Edinburgh community will welcome two speakers, Isabella Deutsch and Jordan Richards, who will discuss Bayesian Statistics in the R programming language.
Day and time: Thursday 27th February 2025, 18:00-19:30
Location: LG.11 40 George Square, The University of Edinburgh, EH8 9LX
Title: Fast and Amortised Bayesian Inference with NeuralEstimators
Abstract
Neural estimators are neural networks that transform data into parameter point estimates. These estimators are likelihood-free and amortised, in the sense that, after an initial setup cost, inference from observed data can be made in a fraction of the time required by conventional approaches, e.g., MCMC or maximum likelihood estimation. They are also approximate Bayes estimators and, therefore, are often referred to as neural Bayes estimators. We present the user-friendly R package NeuralEstimators, which interfaces with the Julia package NeuralEstimators.jl, and allows for the development and application of neural Bayes estimators. This package caters for any model for which simulation is feasible by allowing the user to implicitly define their model via simulated data. No likelihood or long MCMC chains required!
More information here.
π GLE\(^2\)N-sponsored UofE Stats seminar: Ilaria Prosdocimi and Isadora Antoniano Villalobos (Caβ Foscari University of Venice)
The UofE Stats seminar taking place the last week of February is a joint venture with GLE\(^2\)N. See details below.
Day and time: Monday 24th February 15:00
Location: JCMB 5323 and Zoom or via Zoom (Meeting ID: 830 4910 4036 β Passcode: xZHq6w8t)
Title: Modelling non-stationarities in extreme hourly precipitation
Abstract
Understanding spatiotemporal features of extremal precipitation is an essential component of hydrological risk management, for example in flood risk assessment. This study investigates and compares the performance of recently proposed multivariate extreme value models in describing such features, while allowing for spatial variation, temporal non-stationarity and different extremal dependence regimes. We analyse data on hourly precipitation recorded at 65 sites in the period 1990β2023 across the Piave river basin in Northeastern Italy. Empirical evidence suggests that both the extremal dependence structures and the marginal behaviours of precipitation events vary throughout the year, reflecting seasonal patterns. Furthermore spatial dependence appears to weaken as precipitation events become more extreme. We investigate possible exogenous factors by introducing climatic variables as predictors for the marginal distributions, the spatial dependence and the interplay between them. We observe that capturing these features is essential to provide a realistic description of extreme precipitation processes in order to better estimate the risks associated with them.
More information here.