Some authors have recently devised adaptations of spectral grouping algorithms to integrate prior knowledge, as constrained eigenvalues problems. In this paper, we adapt recent statistical grouping algorithms to this task, as a non-parametric mixture model estimation problem. The approach appears to be attractive for its theoretical benefits, and its experimental results, as light bias brings dramatic improvements over unbiased approaches on hard images.