Q. What characteristics contribute to the potential low intrinsic dimensionality of Bayesian inverse problems, as discussed in the Dimensionality Reduction? Check all that apply.

A) The prior distribution on high-dimensional parameters is very smoothing.

B) The observations are not strongly influenced by the parameters of interest.

C) The forward model is linear.

D) The problem has a specific prediction goal.

E) The observations are very local to specific parameters.