Risk-sensitivity conditions for stochastic uncertain model validation

The paper presents sufficient and necessary conditions that verify the relevance of an assumed linear stochastic system model for problems in which probabilistic characteristics of the plant are not known exactly. The approach is to establish the existence of an admissible probability space on which the output of the candidate stochastic system model is consistent (in a stochastic sense) with the noisy output of the plant.