2 citations · 3 across the 3 of their papers we have counts for
5 papers
A PC Algorithm for Max-Linear Bayesian Networks
Carlos Améndola, Benjamin Hollering, Francesco Nowell
Max-linear Bayesian networks (MLBNs) are a relatively recent class of structural equation models which arise when the random variables involved have heavy-tailed distributions. Unl…
Markov Equivalence of Max-Linear Bayesian Networks
Carlos Améndola, Ben Hollering, Seth Sullivant +1
Max-linear Bayesian networks have emerged as highly applicable models for causal inference via extreme value data. However, conditional independence (CI) for max-linear Bayesian ne…
Discrete Max-Linear Bayesian Networks
Benjamin Hollering, Seth Sullivant
Discrete max-linear Bayesian networks are directed graphical models specified by the same recursive structural equations as max-linear models but with discrete innovations. When al…
Identifiability in Phylogenetics using Algebraic Matroids
Benjamin Hollering, Seth Sullivant
Identifiability is a crucial property for a statistical model since distributions in the model uniquely determine the parameters that produce them. In phylogenetics, the identifiab…
Exchangeable and Sampling Consistent Distributions on Rooted Binary Trees
Ben Hollering, Seth Sullivant
We introduce a notion of finite sampling consistency for phylogenetic trees and show that the set of finitely sampling consistent and exchangeable distributions on n leaf phylogene…