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Associated Code for Shaddox et al (2018). A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD. Statistics in Biosciences, 10(1), 59-85.

MATLAB 100.00%

multgraphmodels's Introduction

MultGraphModels

Author: Elin Shaddox

Contact: [email protected]

The provided Matlab files for Bayesian inference of multiple graphical models are associated with the following publication:

Shaddox, E., Stingo, F., Peterson, C.B., Jacobson, S., Cruickshank-Quinn, C., Kechris, K., Bowler, R. and Vannucci, M. (2018). A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD. Statistics in Biosciences, 10(1), 59-85.

These scripts rely on functions from the Matlab code for G-wishart sampling provided by Hao Wang at https://msu.edu/~haowang/ and are associated with the following publications

Associated publications:

H. Wang, Scaling It Up: Stochastic Search Structure Learning in Graphical Models Bayesian Analysis 10 (2015): 351-377

Wang, H. and Li, S. (2012). Efficient Gaussian graphical model determination under G-Wishart prior distributions. Electronic Journal of Statistics. 6: 168—198.

Please cite all publications if you use this code. Thanks!

OVERVIEW OF FILES

Example_multiple_graphs_SSVS.m

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Basic example of running the MCMC sampler and generating results summaries on a simple setting with 3 groups with identical dependence structure

MCMC_multiple_graphs_SSVS_final.m

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Code for running the MCMC sampler

calc_mrf_C.m

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Helper function for calculating the normalizing constant for the MRF prior

generate_sim1_input..m

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Script to generate matrices similar to those used as input to the first Simulation

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