CMStatistics 2021: Start Registration
View Submission - CMStatistics
Title: Bayesian causal graphical models with purely observational data Authors:  Yang Ni - Texas AM University (United States) [presenting]
Abstract: A novel Bayesian causal graphical model approach is presented for reverse-engineering gene regulatory networks based on purely observational genomic data. The proposed model is provably identifiable. Empirical studies support its practical utility.