### log-density of the multivariate normal distribution
ldmvnorm<-function(X,mu,Sigma,iSigma=solve(Sigma),dSigma=det(Sigma)) 
{
  Y<-t( t(X)-mu)
  sum(diag(-.5*t(Y)%*%Y%*%iSigma))  -
  .5*(  prod(dim(X))*log(2*pi) +     dim(X)[1]*log(dSigma) )
                                 
}
###


### sample from the multivariate normal distribution
rmvnorm<-function(n,mu,Sigma)
{
  p<-length(mu)
  res<-matrix(0,nrow=n,ncol=p)
  if( n>0 & p>0 )
  {
    E<-matrix(rnorm(n*p),n,p)
    res<-t(  t(E%*%chol(Sigma)) +c(mu))
  }
  res
}
###


### sample from the Wishart distribution
rwish<-function(n,nu0,S0)
{
  sS0 <- chol(S0)
  S<-array( dim=c( dim(S0),n ) )
  for(i in 1:n)
  {
     Z <- matrix(rnorm(nu0 * dim(S0)[1]), nu0, dim(S0)[1]) %*% sS0
     S[,,i]<- t(Z)%*%Z
  }
  S[,,1:n]
}
###


### tumor example
XY.tumor<-dget("XY.tumor")
###