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.

翻译：提出一种多输出高斯过程（GP）模型，用于基于历史对数预测分数形式的预测结果，在给定协变量向量的条件下，对一组模型和/或专家的局部预测能力的联合后验分布进行建模。在对数分数进行幂变换后，可采用带高斯噪声的GP模型，从而通过两步法实现快速计算：首先使用哈密顿蒙特卡洛方法从已边缘化潜在GP表面的模型中采样GP超参数，然后利用这些采样结果在给定新协变量向量的条件下生成联合预测能力的采样数据。基于学习得到的联合局部预测能力的线性池化方法被应用于预测华盛顿特区每日自行车使用量。