The Matlab files provided for Bayesian inference of multiple graphical models are associated with the following publication:
- Shaddox E, Stingo F, Peterson CB, Jacobson S, Cruickshank-Quinn C, Kechris K, Bowler R, Vannucci M. (2018). A Bayesian approach for learning gene networks underlying disease severity in COPD. Statistics in Biosciences. 10(1): 59-85. [pdf]
This work improves the computational scalability of our previous work on Bayesian inference of multiple Gaussian graphical models by utilizing a continuous shrinkage prior. This enables the current method to scale to >100 nodes.
These scripts rely on code provided with the following prior publications:
- Peterson CB, Stingo FC, Vannucci M. (2015) Bayesian inference of multiple Gaussian graphical models. Journal of the American Statistical Association. 110(509): 159—174.
- Wang H. Scaling it up: stochastic search structure learning in graphical models. Bayesian Analysis. 10 (2015): 351-377
Please cite these publications if you use this code. Thanks!
Basic example of running the MCMC sampler and generating results summaries on a simple setting with 3 groups with identical dependence structure
Code for running the MCMC sampler
Helper function for calculating the normalizing constant for the MRF prior
Script to generate matrices similar to those used as input to the first simulation