The objective is to improve the learning of a regression model of the hydrocracking process using a reduced number of observations. When a new catalyst is used for the hydrocracking process, a new model must be fitted. Generating new data is expensive and therefore it is advantageous to limit the amount of new data generation. Our idea is to use a second data set of measurements made on a process using an old catalyst. This second data set is large enough to fit performing models for the old catalyst. In this work, we use the knowledge from this old catalyst to learn a model on the new catalyst. This task is a transfer learning task. We show that the results are greatly improved with a Bayesian approach to transfer linear model and kriging model.

中文翻译：

---

通过贝叶斯迁移学习减少使用克里金法对加氢裂化过程进行建模所需的实验次数

目标是使用减少的观察次数改进加氢裂化过程的回归模型的学习。当新催化剂用于加氢裂化过程时，必须安装新型号。生成新数据的成本很高，因此限制新数据生成的数量是有利的。我们的想法是在使用旧催化剂的过程中使用第二个测量数据集。第二个数据集足够大，可以拟合旧催化剂的执行模型。在这项工作中，我们使用旧催化剂的知识来学习新催化剂的模型。这个任务是一个迁移学习任务。我们表明，使用贝叶斯方法来传递线性模型和克里金模型，结果得到了极大的改善。