Bayesian methods of analyzing complex count data with applications to microbiome study

I will present two flexible Bayesian methods to analyze complex count data with application to microbiome study. The first project is to develop a Bayesian sparse multivariate regression method that model the relationship between microbe abundance and environmental factors. For the second project, we develop a Bayesian nonparametric regression model for count data with excess zeros. The methods are demonstrated with ocean microbiome data and chronic wound microbiome data.

Speaker: Ju Hee Lee (Juhee Lee) - Associate Professor, Department of Statistics, University of California, Santa Cruz