3 papers
stat.ME2022
Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments
Johan Segers, Stefka Asenova
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 in…
stat.ME2021
Extremes of Markov random fields on block graphs: max-stable limits and structured Hüsler-Reiss distributions
Stefka Asenova, Johan Segers
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 cases,…
stat.ME2020
Inference on extremal dependence in the domain of attraction of a structured Hüsler-Reiss distribution motivated by a Markov tree with latent variables
Stefka Asenova, Gildas Mazo, Johan Segers
A Markov tree is a probabilistic graphical model for a random vector indexed by the nodes of an undirected tree encoding conditional independence relations between variables. One p…