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#' Gibbs Sampling for the Matrix-variate Bingham-von Mises-Fisher Distribution.
#'
#' Simulate a random orthonormal matrix from the Bingham distribution using
#' Gibbs sampling.
#'
#'
#' @param A a symmetric matrix.
#' @param B a diagonal matrix with decreasing entries.
#' @param C a matrix with the same dimension as X.
#' @param X the current value of the random orthonormal matrix.
#' @return a new value of the matrix \code{X} obtained by Gibbs sampling.
#' @note This provides one Gibbs scan. The function should be used iteratively.
#' @author Peter Hoff
#' @references Hoff(2009)
#' @examples
#'
#' ## The function is currently defined as
#' function (A, B, C, X)
#' {
#' m <- dim(X)[1]
#' R <- dim(X)[2]
#' if (m > R) {
#' for (r in sample(seq(1, R, length = R))) {
#' N <- NullC(X[, -r])
#' An <- B[r, r] * t(N) %*% (A) %*% N
#' cn <- t(N) %*% C[, r]
#' X[, r] <- N %*% rbmf.vector.gibbs(An, cn, t(N) %*%
#' X[, r])
#' }
#' }
#' if (m == R) {
#' for (s in seq(1, R, length = R)) {
#' r <- sort(sample(seq(1, R, length = R), 2))
#' N <- NullC(X[, -r])
#' An <- t(N) %*% A %*% N
#' Cn <- t(N) %*% C[, r]
#' X[, r] <- N %*% rbmf.O2(An, B[r, r], Cn)
#' }
#' }
#' X
#' }
#'
#' @export rbmf.matrix.gibbs
rbmf.matrix.gibbs <-
function(A,B,C,X)
{
#simulate from the matrix bmf distribution as described in Hoff(2009)
#this is one Gibbs step, and must be used iteratively
#warning - do not start X at the eigenvectors of A:
#The relative weights on A then can become infinity.
#Instead, you can start close to A, e.g.
# X<-rmf.matrix(UA[,1:R]*m)
m<-dim(X)[1] ; R<-dim(X)[2]
if(m>R)
{
for(r in sample( seq(1,R,length=R)))
{
N<-NullC(X[,-r])
An<-B[r,r]*t(N)%*%(A)%*%N ; cn<- t(N)%*%C[,r]
X[,r]<-N%*%rbmf.vector.gibbs(An,cn,t(N)%*%X[,r])
}
}
if(m==R)
{
for(s in seq(1,R,length=R))
{
r<-sort(sample(seq(1,R,length=R),2))
N<-NullC( X[,-r] )
An<- t(N)%*%A%*%N
Cn<- t(N)%*%C[,r]
X[,r]<-N%*%rbmf.O2(An,B[r,r],Cn)
}
}
X
}
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