In science and engineering, we often work with models designed for accurate
prediction of variables of interest. Recognizing that these models are
approximations of reality, it becomes desirable to apply multiple models to the
same data and integrate their outcomes. In this paper, we operate within the
Bayesian paradigm, relying on Gaussian processes as our models. These models
generate predictive probability density functions (pdfs), and the objective is
to integrate them systematically, employing both linear and log-linear pooling.
We introduce novel approaches for log-linear pooling, determining
input-dependent weights for the predictive pdfs of the Gaussian processes. The
aggregation of the pdfs is realized through Monte Carlo sampling, drawing
samples of weights from their posterior. The performance of these methods, as
well as those based on linear pooling, is demonstrated using a synthetic
dataset.

本文介绍了应用于科学和工程领域的模型，利用贝叶斯范式和高斯过程生成概率密度函数（pdfs），通过蒙特卡洛抽样来集成这些 pdfs，并通过线性和对数线性汇集方法介绍了对预测 pdfs 的输入相关权重的确定方法，使用合成数据集验证了这些方法的性能。