  A multi-output Gaussian process (GP) is introduced as a model for the joint
posterior distribution of the local predictive ability of set of models and/or
experts, conditional on a vector of covariates, from historical predictions in
the form of log predictive scores. Following a power transformation of the log
scores, a GP with Gaussian noise can be used, which allows faster computation
by first using Hamiltonian Monte Carlo to sample the hyper-parameters of the GP
from a model where the latent GP surface has been marginalized out, and then
using these draws to generate draws of joint predictive ability conditional on
a new vector of covariates. Linear pools based on learned joint local
predictive ability are applied to predict daily bike usage in Washington DC.
