UCPH Statistics Seminar: Pratik Misra

Title: Structural identifiability in Gaussian graphical models

Speaker: Pratik Misra (Binghamton University, State University of New York)

Abstract: Gaussian graphical models are a key area of research in Algebraic Statistics where the dependence structure between jointly normal random variables is determined by a graph. In this talk, I will present the problem of structural identifiability in Gaussian DAG models. I will show how imposing symmetry conditions on the model parameters can guarantee identifiability. Building on prior work by Peters and Bühlmann and by Wu and Drton, we establish structural identifiability under homogeneous structural coefficients, as well as for a broader class of models with partially homogeneous structural coefficients called BPEC-DAGs. In the end, I will present an analogue of the GES algorithm for learning BPEC-DAGs and evaluate its performance on real datasets.