activity
20182022
most citedSymmetrically colored Gaussian graphical models with toric vanishing ideal

4 citations · 8 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ME2022★ 1 cited

Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networks

Danai Deligeorgaki, Alex Markham, Pratik Misra +1

We consider the problem of estimating the marginal independence structure of a Bayesian network from observational data, learning an undirected graph we call the unconditional depe…

stat.ML2022★ 1 cited

A Transformational Characterization of Unconditionally Equivalent Bayesian Networks

Alex Markham, Danai Deligeorgaki, Pratik Misra +1

We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i.e., when directed acyclic graphs (DAGs) have the same set of unconditional -separa…

math.CO2021★ 4 cited

Symmetrically colored Gaussian graphical models with toric vanishing ideal

Jane Ivy Coons, Aida Maraj, Pratik Misra +1

A colored Gaussian graphical model is a linear concentration model in which equalities among the concentrations are specified by a coloring of an underlying graph. The model is cal…

math.AC2021★ 2 cited

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…

math.AG2019

Gaussian graphical models with toric vanishing ideals

Pratik Misra, Seth Sullivant

Gaussian graphical models are semi-algebraic subsets of the cone of positive definite covariance matrices. They are widely used throughout natural sciences, computational biology a…

math.CO2018

Bounds on the expected size of the maximum agreement subtree for a given tree shape

Pratik Misra, Seth Sullivant

We show that the expected size of the maximum agreement subtree of two -leaf trees, uniformly random among all trees with the shape, is . To derive the lower bound,…