Online Variational Bayes Inference for High-Dimensional Correlated Data

The analysis of such data poses many computational challenges, especially when the observations are correlated over time and/or across space. In this article, we propose flexible hierarchical regression models for analyzing such data that accommodate serial and/or spatial correlation.

We address the computational challenges involved in fitting these models by adopting an approximate inference framework. We develop an online variational Bayes algorithm that works by incrementally reading the data into memory one portion at a time. The performance of the method is assessed through simulation studies.