Search Results for author:
Found 7 papers, 0 papers with code
Bayesian Approach to Linear Bayesian Networks
The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix.
Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances
It has been shown that linear Gaussian structural equation models are fully identifiable if all error variances are the same or known.
High-Dimensional Poisson DAG Model Learning Using $\ell_1$-Regularized Regression
In this paper, we develop a new approach to learning high-dimensional Poisson directed acyclic graphical (DAG) models from only observational data without strong assumptions such as faithfulness and strong sparsity.
Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models
We introduce a new class of identifiable DAG models where the conditional distribution of each node given its parents belongs to a family of generalized hypergeometric distributions (GHD).
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS)
We prove that this class of QVF DAG models is identifiable, and introduce a new algorithm, the OverDispersion Scoring (ODS) algorithm, for learning large-scale QVF DAG models.
Identifiability Assumptions and Algorithm for Directed Graphical Models with Feedback
Our simulation study supports our theoretical results, showing that the algorithms based on our two new principles generally out-perform algorithms based on the faithfulness assumption in terms of selecting the true skeleton for DCG models.
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring
In this paper, we address the question of identifiability and learning algorithms for large-scale Poisson Directed Acyclic Graphical (DAG) models.
