We study the problem of completing partially known state statistics of large-scale linear systems. The dynamical interaction between state variables is known while the directionality of input excitation is uncertain. In particular, we seek to explain the data with the least number of possible input disturbance channels. This can be formulated as a rank minimization problem, and for its solution, we employ a convex relaxation based on the nuclear norm.