This paper considers nonlinear estimation problems for a class of models, and employs relative entropy to describe the uncertainty classes. Two problems are formulated and their solutions are sought. 1) When the transition probability between the signal to be estimated X and the measurement Y or stochastic kernel is unknown, and 2) when the joint probability induced by the R.V.'s X, Y is unknown. For both problems, the uncertainty is described by a relative entropy constraint between the unknown distribution and a fixed nominal distribution. The solutions provided bring forward some properties associated with the estimate of the true distribution. Classical examples are chosen to illustrate the applicability of the results.