52 Efficient Bayesian Additive Regression Models for Microbiome and Gene Expression Studies

Oregon Convention Center 

We propose a flexible family of Bayesian multinomial logistic-normal additive Gaussian process regression (MLN) models for estimating additive linear and non-linear effects in microbiome and gene expression studies. This family has a marginally latent matrix-t process (MLTP) form, facilitating efficient and accurate inference via a particle filter with marginal Laplace approximation. We also develop a maximum marginal likelihood estimation method for model hyperparameters. We demonstrate the efficiency and utility of these models for estimating linear and non-linear effects through analyses of real and simulated sequence count data.

Bayesian Statistics

Nonlinear Regression

Gaussian Processes

Microbiome Data

Gene Expression Data