2017/11/02 by Zehang Richard Li, Zehang Li, Li, Zehang Richard +4 · 1 citation
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #stat.AP
paper · pdf · doi:10.48550/arxiv.1711.00877
openalex publication_date 2017/11/02 · arxiv created 2019/07/24 · arxiv updated 2019/07/25 · openalex created_date 2022/10/07 · openalex updated_date 2026/07/28
Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics. Existing methods, however, typically rely on copious, high quality data to accurately learn associations. In this paper, we develop a method for scientific settings where learning dependence structure is essential, but data are sparse and have a high fraction of missing values. Specifically, our work is motivated by survey-based cause of death assessments known as verbal autopsies (VAs). We propose a Bayesian approach to characterize dependence relationships using a latent Gaussian graphical model that incorporates informative priors on the marginal distributions of the variables. We demonstrate such information can improve estimation of the dependence structure, especially in settings with little training data. We show that our method can be integrated into existing probabilistic cause-of-death assignment algorithms and improves model performance while recovering dependence patterns between symptoms that can inform efficient questionnaire design in future data collection.