89 citations · 471 across the 59 of their papers we have counts for
69 papers
Identifiability of a Simple Model of Lateral Gene Transfer
Devon Olds, Seth Sullivant
In evolutionary biology, factors like lateral gene transfer complicate the tree of life, making inference of a species tree more difficult. The phenomenon of lateral gene transfer…
Structural Identifiability of Series-Parallel LCR Systems
Cashous Bortner, Seth Sullivant
We consider the identifiability problem for the parameters of series-parallel LCR circuit networks. We prove that for networks with only two classes of components (inductor-capacit…
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…
Identifiability of linear compartmental tree models and a general formula for input-output equations
Cashous Bortner, Elizabeth Gross, Nicolette Meshkat +2
A foundational question in the theory of linear compartmental models is how to assess whether a model is structurally identifiable -- that is, whether parameter values can be infer…
Directed Gaussian graphical models with toric vanishing ideals
Pratik Misra, Seth Sullivant
Directed Gaussian graphical models are statistical models that use a directed acyclic graph (DAG) to represent the conditional independence structures between a set of jointly norm…
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…