4 papers
Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs
Hyunwoong Chang, Fariha Taskin
We propose a joint order-based scoring framework for causal structure learning of directed acyclic graph (DAG) models under heterogeneous data settings. We show that leveraging het…
A Bayesian Framework for Symmetry Inference in Chaotic Attractors
Ziad Ghanem, Chang Hyunwoong, Preskella Mrad
Detecting symmetry from data is a fundamental problem in signal analysis, providing insight into underlying structure and constraints. When data emerge as trajectories of dynamical…
Identifiability of the minimum-trace directed acyclic graph and hill climbing algorithms without strict local optima under weakly increasing error variances
Hyunwoong Chang, Jaehoan Kim
We prove that the true underlying directed acyclic graph (DAG) in Gaussian linear structural equation models is identifiable as the minimum-trace DAG when the error variances are w…
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces
Hyunwoong Chang, Quan Zhou
Convergence analysis of Markov chain Monte Carlo methods in high-dimensional statistical applications is increasingly recognized. In this paper, we develop general mixing time boun…