no code implementations • 12 Feb 2024 • Felix Leopoldo Rios, Alex Markham, Liam Solus
Several approaches to graphically representing context-specific relations among jointly distributed categorical variables have been proposed, along with structure learning algorithms.
no code implementations • 31 May 2023 • Alex Markham, MingYu Liu, Bryon Aragam, Liam Solus
Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences.
no code implementations • 3 Oct 2022 • Danai Deligeorgaki, Alex Markham, Pratik Misra, Liam Solus
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 dependence graph.
no code implementations • 1 Mar 2022 • Alex Markham, Danai Deligeorgaki, Pratik Misra, Liam Solus
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 $d$-separation statements.
no code implementations • 5 Mar 2021 • Svante Linusson, Petter Restadh, Liam Solus
We show that the moves of the aforementioned algorithms are included within classes of edges of $\operatorname{CIM}_p$ and that restrictions placed on the skeleton of the candidate DAGs correspond to faces of $\operatorname{CIM}_p$.
Causal Discovery Statistics Theory Combinatorics Statistics Theory
no code implementations • 22 Jan 2021 • Eliana Duarte, Liam Solus
We consider the problem of representing causal models that encode context-specific information for discrete data using a proper subclass of staged tree models which we call CStrees.
no code implementations • NeurIPS 2017 • Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler
Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale.