Considering New approaches

Multiple Structural Models

I understand that it is possible to use more than one structural model and am therefore exploring the possibility of multiple likelihoods. I am not familiary with the matehmatics on this yet. However even with this approach an issue still remains insofar that I am trying to model principal components and as mentioned these dont have intuitive meaning as they existing in a transformed vector space.

The original goal was to produce a posterior distribution for the principal components. From this values could be drawn when performing simulations, a reverse transformation of these values into yield curve movements, which then generates a future yield curves. If I stick with this approach then perhaps I am still left with the problem of "no intuitive meaning" in trying to set priors. But perhaps the flexibillity of multiple likelihoods may help.

Transforming intuitive statements into multiple priors

An alternative may be to continue to think about deriving priors from intutiive statements by some transformation process... but I dont want this to be too complex, and I would like it to be able to accomodate multiple types expert judgements and so a one size fits all solution presents a challenge.