A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: Beyond edge weights in psychological networks
2017
2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Uncertainty over model structures poses a challenge for many approaches exploring effect strength parameters at system-level. Monte Carlo methods for full Bayesian model averaging over model structures require considerable computational resources, whereas bootstrapped graphical lasso and its approximations offer scalable alternatives with lower complexity. Although the computational efficiency of graphical lasso based approaches has prompted growing number of applications, the restrictive
doi:10.1109/cibcb.2017.8058566
dblp:conf/cibcb/HullamJDA17
fatcat:a67mkpbvwbeyzhot3iwxj6tjwi