It seems that interest rises to know the in our predictions. For example this finds the uncertainty of a prediction by a . Or this paper finds the uncertainties of a network used in computer vision. In general, Gaussian processes are famous for getting the uncertainty in case of non parametric regression.

My question about all these uncertainties is:

Is uncertainty a property of these methods and these models or does uncertainty on the model choice?

It follows from this thought experiment:

Let’s say we have a data set. A fixed data set. We fit a) a Bayesian random forest b) a neural network c) a to this data. Now I get a new input, x. I wonder if all three models would give the same uncertainty about the prediction on data point x.



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