View source: R/mcmc_functions.R
mvnorm_sd | R Documentation |
This draws a sample from a multivariate normal distribution with mean vector mu and covariance matrix Sigma. It requires the covariance matrix to be decomposed using spectral decomposition (eigen).
mvnorm_sd(mu, decomp.covariance)
mu |
The mean vector |
decomp.covariance |
This spectral decomposition part of the sampler. It is VU^0.5, where Sigma = VU*t(V). The required component is returned by the construct_constrained_covariance_matrix function. |
a vector containing a sample from the distribution
mu <- c(2, 1) #mean vector sigma <- matrix(c(2^2, 0.5*2*1, 0.5*2*1, 1^2), 2, 2) #covariacne matrix sigma.eigen <- eigen(sigma) decomp.covariance <- sigma.eigen$vectors%*%diag(sqrt(sigma.eigen$values)) f <- mvnorm_sd(mu, decomp.covariance) #draw sample
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