Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
Statistics Theory
2022-08-02 v1 Machine Learning
Machine Learning
Statistics Theory
Abstract
The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix. However, these polynomials can be exponentially large, making them impractical for many purposes. In this paper, we present a graphical notation for many of these polynomial constraints. The expressive power of this notation is investigated both theoretically and empirically.
Cite
@article{arxiv.2208.00926,
title = {Graphical Representations for Algebraic Constraints of Linear Structural Equations Models},
author = {Thijs van Ommen and Mathias Drton},
journal= {arXiv preprint arXiv:2208.00926},
year = {2022}
}
Comments
To appear in the proceedings of the 11th International Conference on Probabilistic Graphical Models (PGM 2022)