n<-200
y <- 0.9
mu <- 1:2
Sigma <- y
rmvn.Choleski <-
  function(n, mu, Sigma) {

    # generate n random vectors from MVN(mu, Sigma)
    # dimension is inferred from mu and Sigma
    d <- length(mu)
    Q <- chol(Sigma) # Choleski factorization of Sigma
    X <- matrix(rnorm(n*d), nrow=n, ncol=d)
    U <- Z %*% Q + matrix(mu, n, d, byrow=TRUE)
  }
X <- rmvn.Choleski(n, mu, Sigma)
pairs(U)
T1<- -log(U1)
T2<- -log(U2)