Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies

DOI10.1214/19-BA1172zbMath1459.62096arXiv1711.00877OpenAlexW2975699996WikidataQ104065544 ScholiaQ104065544MaRDI QIDQ2226709

Tyler H. McComick, Zehang Richard Li, Samuel J. Clark

Publication date: 9 February 2021

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.00877

zbMATH Keywords

mixed datahigh dimensionalspike-and-slabparameter expansioncause of death

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Population dynamics (general) (92D25) Reliability and life testing (62N05) Probabilistic graphical models (62H22)

Related Items (6)

Bayesian graphical models for modern biological applications ⋮ Discussion to: \textit{Bayesian graphical models for modern biological applications} by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo ⋮ Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM ⋮ Bayesian analysis of nonparanormal graphical models using rank-likelihood ⋮ Exact test theory in Gaussian graphical models ⋮ Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques

Uses Software

• R
• bfa
• BDgraph
• HdBCS
• EMVS
• openVA
• GHS

Cites Work

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