Random Generation from a Truncated Conditional Normal Distribution
Samples from the Truncated Conditional Normal Distribution
rtcmvnorm(n = 1, mean = 0, V = 1, x=0, keep=1, lower = -Inf, upper = Inf)
n |
integer: number of samples to be drawn |
mean |
vector of means |
V |
covariance matrix |
x |
vector of observations to condition on |
keep |
element of x to be sampled |
lower |
left truncation point |
upper |
right truncation point |
vector
Jarrod Hadfield j.hadfield@ed.ac.uk
par(mfrow=c(2,1)) V1<-cbind(c(1,0.5), c(0.5,1)) x1<-rtcmvnorm(10000, c(0,0), V=V1, c(0,2), keep=1, lower=-1, upper=1) x2<-rtnorm(10000, 0, 1, lower=-1, upper=1) plot(density(x1), main="Correlated conditioning observation") lines(density(x2), col="red") # denisties of conditional (black) and unconditional (red) distribution # when the two variables are correlated (r=0.5) V2<-diag(2) x3<-rtcmvnorm(10000, c(0,0), V=V2, c(0,2), keep=1, lower=-1, upper=1) x4<-rtnorm(10000, 0, 1, lower=-1, upper=1) plot(density(x3), main="Uncorrelated conditioning observation") lines(density(x4), col="red") # denisties of conditional (black) and unconditional (red) distribution # when the two variables are uncorrelated (r=0)
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