Measuring model uncertainty
I have a model with a number of parameters. I can calculate the posterior distribution of the parameter vector. This, in part, means that I can calculate the expected value and the variance of each parameter.
Question. How do I calculate model uncertainty as a single quantity? While I have some ad hoc ideas, I'd like to take a principled approach.
One idea is to look at the variance of the likelihood: ? I'm not sure how to approach this, other than numerically.
Another idea is to compute something like this:
Any and all help appreciated. :)