blockBGR {baygel} | R Documentation |
Bayesian graphical ridge block Gibbs sampler for Gaussian graphical models.
Description
Implements a Bayesian graphical ridge block Gibbs sampler to simulate the posterior distribution of the precision matrix for Gaussian graphical models.
Usage
blockBGR(X, burnin, iterations, sig = 1, verbose = TRUE)
Arguments
X |
A numeric matrix, assumed to be generated from a multivariate Gaussian distribution. |
burnin |
An integer representing the number of burn-in iterations. |
iterations |
An integer representing the length of the Markov chain post burn-in. |
sig |
A numeric value representing the standard deviation parameter for the double Gaussian and truncated Gaussian prior. |
verbose |
A logical indicating if the MCMC sampler progress should be printed. |
Value
A list containing precision 'Omega' and covariance 'Sigma' matrices from the Markov chains.
Examples
# Generate true precision matrix:
p <- 10
n <- 500
OmegaTrue <- pracma::Toeplitz(c(0.7^rep(1:p-1)))
SigTrue <- pracma::inv(OmegaTrue)
# Generate expected value vector:
mu <- rep(0,p)
# Generate multivariate normal distribution:
set.seed(123)
X <- MASS::mvrnorm(n, mu = mu, Sigma = SigTrue)
# Generate posterior distribution:
posterior <- blockBGR(X, iterations = 1000, burnin = 500, sig = 0.5)
# Estimated precision matrix using the mean of the posterior:
OmegaEst <- apply(simplify2array(posterior$Omega), 1:2, mean)
[Package baygel version 0.3.0 Index]