Augment hyperposterior sampling by also sampling observed values

[kiudee]

In the current implementation, we sample the hyperparameters of the Gaussian process and average across those samples. The training data is fixed. In BoTorch, the hyperparameters are fixed and the observations y of the data points are sampled, and the acquisition function is averaged over those.

I think both ideas can be combined, allowing the user to request:

• Holding both hyperparameters and observations fixed: This will yield the classical acquisition functions for the noiseless case.
• Sampling only the observations y. This will yield the noisy acquisition function versions, assuming that the hyperparameters are accurate.
• Sampling only the hyperparameters. This will yield the current behavior, where we are robust to model misspecification.
• Sampling both the hyperparameters and the observations. Combining the strengths of both approaches.

The steps required are:

• Implement a fast method for cloning the GP model using the same, or different hyperparameters.
• Add the different methods for averaging to

  bayes-skopt/bask/acquisition.py

  Line 48 in 8f1daf9

  def evaluate_acquisitions(