in the case of the mixed linear model the random effects are usually assumed to be normally distributed in both the bayesian and classical frameworks . in this paper , the dirichlet process prior was used to provide nonparametric bayesian estimates for correlated random effects . 
 this goal was achieved by providing a gibbs sampler algorithm that allows these correlated random effects to have a nonparametric prior distribution . 
 a sampling based method is illustrated . 
 this method which is employed by transforming the genetic covariance matrix to an identity matrix so that the random effects are uncorrelated , is an extension of the theory and the results of previous researchers . also by using gibbs sampling and data augmentation 
 a simulation procedure was derived for estimating the precision parameter m associated with the dirichlet process prior . 
 all needed conditional posterior distributions are given . 
 to illustrate the application , data from the elsenburg dormer sheep stud were analysed . 
 a total of 3325 weaning weight records from the progeny of 101 sires were used .