I have this confusion related to the benefits of Gaussian processes. I mean comparing it to simple linear regression, where we have defined that the linear function models the data.
However, in Gaussian processes we define the distribution of the functions means we don't specifically define that the function should be linear. We can define a prior over the function which is the Gaussian prior which defines features like how much smooth the function should be and all.
So we don't have to explicitly define what the model should be. However, I have questions. We do have marginal likelihood and using it we can tune the covariance function parameters of the gaussian prior. So this is similar to defining the type of function that it should be isn't it.
It boils down to the same thing defining the parameters even though in GP they are hyperparameters. For eg in this paper. They have defined that the mean function of the GP is something like
$$m(x) = ax ^2 + bx + c \quad \text{i.e. a second order polynomial.}$$
So definitely the model/function is defined isn't it. So what's the difference in defining the function to be linear like in the LR.
I just didn't get what the benefit is of using GP