README.md

In garyganuiuc/BayesEM: Bayesian EM algorithms for Learning the Sparsity of Gaussian Graphical Models

BayesEM

R package (Bayes EM algorithms for Gaussian Graphical Models)

Maintainer

Lingrui(Gary) Gan, University of Illinois at Urbana and Champaign

Feng Liang, University of Illinois at Urbana and Champaign

Description

Fast Bayesian algorithms for learning the sparse structure of a Gaussian graphical model

Example

Simulated Samples from AR(1) Structure

C=toeplitz(c(1,0.5,rep(0,p_n-2)))
Sigma=solve(C)
Y<-mvrnorm(n,rep(0,p_n),Sigma)
S<-cov(Y) 

Learning the Sparse Structure From BayesEM Algorithms

Hyperparameters

a0=1
b0=1
alpha=1
beta=c(1,50,100)


v0=0.1
v1=c(0.3,0.4,0.5)

maxiter=100
Ra=3000

Tune the parameters

Tune=Tune_EMLasso(S,n,p_n,a0,b0,alpha,beta,v0,maxiter,w,l,Ra)
Ra=Tune$Ra
v1=Tune$v1
beta=Tune$beta

Implement BayesEM Algorithm for the Sparse Learning

result<-EM(S,n,p_n,a0,b0,alpha,beta,v0,v1,maxiter,w,l,Ra)

Outputs

The marginal posterior probability matrix of each entries

result$P

The MAP estimate of the precision matrix

result$Theta

Reference

Manuscript

garyganuiuc/BayesEM documentation built on May 16, 2019, 5:43 p.m.