I find it hard to distinguish between these two concepts. In a variational inference setting we learn a distribution over the parameters of our function. in the definition of Gaussian processes we learn a distribution over functions. I wonder, when we have a distribution over the parameters of a function, then we have different functions, right? then I am unable to go further and tell the definition of Gaussian processes from the definition of a the posterior of a Variational inference apart. Would you please help me to clarify these two?
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