  We present a modified Gibbs sampler for general state spaces. We establish
that this modification can lead to substantial gains in statistical efficiency
while maintaining the overall quality of convergence. We illustrate our results
in two examples including a toy Normal-Normal model and a Bayesian version of
the random effects model.
