Dear Professors,
In addition to parameter calibration, can the Bayesian inference module in UQLab be used to make predictions?
best wishes
Student
Dear @Mr121Tony
It is not exactly clear to me what you mean by “making prediction”. Are you interested in the predictive distribution? If yes, I suggest you to first have a look at the manual:
Let us know if this is not what you’re interested in or if you are stuck with the implementation.
Best regards
Styfen
In UQLab - Bayesian inversion user manual, there are model predictions as shown in the picture. How can I use it to make predictions in a real case?
Dear @Mr121Tony ,
if you are interested in making predictions in a “real case”, you have to generate a BayesOpts object as described in Section 3.1 of the manual describing the “real case” and use this as input for uq_createAnalysis. Using the .Results.PostProc.PostSample-component of the struct resulting from this this function call one can compute predictions, which I prefer to denote as performing forward UQ.
Following the suggestion by Paul-Remo in https://uqworld.org/t/looking-for-documentation-example-of-using-uqlab-results-of-bayesian-inference-for-forward-uq-or-further-bayesian-inference/690/2?u=olaf.klein I extract 2D-arrays containing sample-vectors of
the values of the different parameters combined with a value for the computed discrepancy. Afterwards I use these sample-vectors to perform Forward UQ
similar to the method the posterior predictive samples are computed in UQLab:
for each sample-vector I computed the value for my model for the given set of
parameter values and afterwards noise was added by computing a noise sample
for a normal distributed error according to the corresponding discrepancy value to
get a sample for the posterior predictive density.
Greetings
Olaf
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