BAGEL: a Bayesian graphical model for inferring drug effect longitudinally on depression in people with HIV

DOI10.1214/21-AOAS1492zbMath1498.62238arXiv2007.01484MaRDI QIDQ2135329

Yanxun Xu, Amanda B. Spence, Leah H. Rubin, Yang Ni, Yuliang Li

Publication date: 6 May 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.01484

zbMATH Keywords

Bayesian nonparametricsgraphical modeldepressionprecision medicinelongitudinal cohort study

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Bayesian inference (62F15) Probabilistic graphical models (62H22)

Uses Software

• CODA

Cites Work

• Unnamed Item
• Understanding predictive information criteria for Bayesian models
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Nonparametric Bayesian data analysis
• Prior distributions on spaces of probability measures
• Simultaneous probability statements for Bayesian P-splines
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Gene Expression and Proteomics

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