I have the following problem I'm reading through the Gaussian Process book http://www.gaussianprocess.org/gpml/chapters/RW2.pdf ,now coming the questions,in the bayesian linear regression,why can someone assume that the prior over the parameters is always Gaussian. the same to Gaussian process regression, for a training set , we don't know the distribution of the process,maybe not gaussian, can GPR always work well? I just don't understand and somebady else ask me why? why gaussian? Is there any paper or book? help,please,thankyou!

I have the following problem. I'm reading through the Gaussian Process book http://www.gaussianprocess.org/gpml/chapters/RW2.pdf. In the bayesian linear regression it is suggested to use the Gaussian prior over the parameters. For the Gaussian process regression we also don't know the distribution of the process, it can be not gaussian. Can GPR always work well? I just don't understand why should we use gaussian distribution? Is there any paper or book?