Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies.

ABSTRACT: 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 esti

SUBMITTER: Li ZR 

PROVIDER: S-EPMC7709479 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature