Description Usage Arguments Details Value Author(s) Examples
Generate Standard Gaussian samples with a Gaussian transiiton kernel
1 |
X |
the seeds for the Markov Chain. There are as many MC drawn as given seeds |
N |
the number of desired samples"' |
thinning |
the proportion of kept samples, ie. 1 each |
sigma |
the exploration parameter for the transition kernel |
lsf |
a boolean limit-state function for definig a subdomain of the input space. |
burnin |
the |
This function generates standard Gaussian samples with a Markov Chain using a suitable transition kernel
A matrix X
with the number of desired samples
Clement WALTER clementwalter@icloud.com
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # Get a seed in dimension 2
X <- matrix(rnorm(2), nrow = 2)
X <- generateK(X, N = 1000)
library(ggplot2)
ggplot(as.data.frame(t(X)), aes(x_1,x_2)) + geom_point()
# One can also specify a limit-state function
lsf <- function(X){
sqrt(colSums(X^2)) > 2
}
X <- matrix(c(2, 2), nrow = 2)
X <- generateK(X, N = 1000, lsf = lsf)
ggplot(as.data.frame(t(X)), aes(x_1,x_2)) + geom_point()
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