Conditional independence, graphical models and sparsity are key notions for parsimonious
statistical models and for understanding the structural relationships in the data. The theory of …

Conditional independence and graphical models are well studied for probability
distributions on product spaces. We propose a new notion of conditional independence for …

Extremal graphical models encode the conditional independence structure of multivariate
extremes. For the popular class of H\" usler--Reiss models, we propose a majority voting …

Recursive max-linear vectors provide models for the causal dependence between large
values of observed random variables as they are supported on directed acyclic graphs …

Graphical models in extremes have emerged as a diverse and quickly expanding research
area in extremal dependence modeling. They allow for parsimonious statistical methodology …

Abstract Support Vector Machines (SVMs) are one of the most popular supervised learning
models to classify using a hyperplane in an Euclidean space. Similar to SVMs, tropical …

Environmental data science for spatial extremes has traditionally relied heavily on max-
stable processes. Even though the popularity of these models has perhaps peaked with …

We study the joint occurrence of large values of a Markov random field or undirected
graphical model associated to a block graph. On such graphs, containing trees as special …

Graphical models with heavy-tailed factors can be used to model extremal dependence or
causality between extreme events. In a Bayesian network, variables are recursively defined …

Recursive max-linear vectors model causal dependence between node variables by a
structural equation model, expressing each node variable as a max-linear function of its …