Comparing dependent Gaussian directed networks

Oregon Convention Center 

We propose an approach to assess stability of Gaussian directed networks with networks constructed based on data from two time points. The proposed approaches unify network construction and comparison. A penalty introduced to the calculation of conditional posterior probability mass function for network differentiation ensures convergence to the underlying truth in probability. Simulations and real data applications support the feasibility of the method and the advantage of addressing dependence in network comparisons.

Gaussian network comparisons

Bayesian methods

Penalized conditional posterior probability

Variable selection. 

Section on Bayesian Statistical Science