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Comparing dependent Gaussian directed networks

Abstract Number:

3324 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Hongmei Zhang (1), Xianzheng Huang (2), Hasan Arshad (3)

Institutions:

(1) University of Memphis, N/A, (2) University of South Carolina, N/A, (3) University of Southampton, N/A

Co-Author(s):

Xianzheng Huang  
University of South Carolina
Hasan Arshad  
University of Southampton

First Author:

Hongmei Zhang  
University of Memphis

Presenting Author:

Hongmei Zhang  
University of Memphis

Abstract Text:

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.

Keywords:

Gaussian network comparisons|Bayesian methods|Penalized conditional posterior probability|Variable selection. | |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Unsupervised Learning

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