Approaches to the estimation of Bayesian networks for high-dimensional data with complex mean structure

Methods for the estimation of Bayesian networks, flexible frameworks allowing the representation of conditional independence relationships of sets of variables, typically require a data set that
consists of independent and identically distributed samples.
However, often the data set available will have a more complex mean structure and additional components of variance, which must be accounted for in the estimation of a Bayesian network. In this talk, I will discuss two approaches to the estimation of Bayesian networks given such data
sets. These approaches will be compared, and their use demonstrated through their application to a gene expression data set that contains
data on covariates thought to affect gene expression levels.