Hello UQlab,
I am working on a bayesian inversion problem.
- Consisting of different forward models (\mathbf{u^+} \mathbf{y^+}, c_f etc).
- I have figured how to make room for different forward models, on the assumption that the model discrepancy is independent and unrelated.
- However, I would like to toy around with the possibility of a dependent covariance matrix. The Question;
- I found one example for a user-defined likelihood function. In the same way I define multiple forward models, can I define multiple user-defined likelihood functions?
- is it possible to go about this differently? i.e. instead of a user-defined likelihood function, my forward model would take the discrepancy “kernel” into consideration (and to close it out, I add a model discrepancy term)
- In (Bayesian Uncertainty Quantification applied to RANS turbulence models), a multiplicative discrepancy is assumed by the authors for the velocity term. I would like to replicate this example. I would like tips on how this can be done.
thank you very much for the kind assistance.