Bayesian networks are graphical models that represent the joint distribution of a set of variables using directed acyclic graphs. The graph can be manually built by domain experts according to their knowledge. However, when the dependence structure is unknown (or partially known) the network has...

A new method is proposed for testing multiple hypotheses with equality and inequality constraints on the parameters of interest. The method is based on the fractional Bayes factor with a modification that the updated prior is centered on the boundary of the constrained parameter space under...

Multi-way tensor data are prevalent in many scientific areas such as genomics and biomedical imaging. We consider a K-way tensor-normal distribution, where the precision matrix for each way has a graphical interpretation. We develop an l1 penalized maximum likelihood estimation and an efficient...

Motivated by analysis of gene expression data measured over different tissues or over time, we consider matrix-valued random variable and matrix-normal distribution, where the precision matrices have a graphical interpretation for genes and tissues, respectively. We present a l1 penalized...

In this chapter, some of the many prominent and recent papers in the systemic risk literature are reviewed. In all these papers, financial econometrics methods are used whether to extract the connections between institutions or assets by analyzing the related data or to construct a measure of...