  Differential networks (DN) are important tools for modeling the changes in
conditional dependencies between multiple samples. A Bayesian approach for
estimating DNs, from the classical viewpoint, is introduced with a
computationally efficient threshold selection for graphical model
determination. The algorithm separately estimates the precision matrices of the
DN using the Bayesian adaptive graphical lasso procedure. Synthetic experiments
illustrate that the Bayesian DN performs exceptionally well in numerical
accuracy and graphical structure determination in comparison to
state-of-the-art methods. The proposed method is applied to South African
COVID-$19$ data to investigate the change in DN structure between various
phases of the pandemic.
