Figure 27.

Schematic of the construction and use of a committee model for uncertainty estimation in sparse GP models, as described in ref (202). Similar graphical representations are used as in Figures 4 and 5: here, multiple models are trained using the same representative set but different random subselections of the training set, y_j. The cost of training scales linearly with the number of committee members, n_c, and each training yields a different weight vector, c_j. When performing a prediction, a single vector of kernels, k, needs to be evaluated (which is usually the computationally intensive task for prediction), and multiple predictions, ỹ_j, can be obtained cheaply by taking scalar products of k with the individual weight vectors corresponding to the members of the committee. Example applications of this methodology are shown in Figure 28.