ArticleBayesian analysis2020
Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies.
Article in Bayesian analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bayesian Tensor Decomposition for Clustering Latent Symptom Profiles for Verbal Autopsy Data.Statistics in medicine · 2026Article
- Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.Biostatistics (Oxford, England) · 2024Article
- BAYESIAN NESTED LATENT CLASS MODELS FOR CAUSE-OF-DEATH ASSIGNMENT USING VERBAL AUTOPSIES ACROSS MULTIPLE DOMAINS.The annals of applied statistics · 2024Article
- Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques.Computational statistics · 2023Article
- The openVA Toolkit for Verbal Autopsies.The R journal · 2022Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
- Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models.Statistical analysis and data mining · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies.Bayesian analysis · 2020Article
- Bayesian Joint Spike-and-Slab Graphical Lasso.Proceedings of machine learning research · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.