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B1187
Title: Variational Bayes for model averaging for multivariate models using compositional predictors Authors:  Alex Lewin - London School of Hygiene and Tropical Medicine (United Kingdom) [presenting]
Abstract: High-throughput technology for molecular biomarkers produces multivariate data exhibiting strong correlation structures and thus should be analysed in an integrated manner. Bayesian models are strongly suited to this aim. A particular case of interest is microbiome data, which is inherently compositional, and thus imposes a constraint on model space. A Bayesian model is presented for multivariate analysis of high-dimensional outcomes and high-dimensional predictors, including compositional predictors. The model includes sparsity in feature selection for predictors and covariance selection. A model averaging approach is taken to ensure a robust selection of predictors. A hybrid Variational Bayes - Monte Carlo computational approach is used for the compositional data updates.