Sequential algorithms are popular for experimental design, enabling emulation, optimisation and inference to be efficiently performed. For most of these applications bespoke software has been developed, but the approach is general and many of the actual computations performed in such software are identical. Motivated by the diverse problems that can in principle be solved with common code, this paper presents GaussED, a simple probabilistic programming language coupled to a powerful experimental design engine, which together automate sequential experimental design for approximating a (possibly nonlinear) quantity of interest in Gaussian processes models. Using a handful of commands, GaussED can be used to: solve linear partial differential equations, perform tomographic reconstruction from integral data and implement Bayesian optimisation with gradient data.

翻译：序列算法在实验设计中很受欢迎,可以使模拟、优化和推导有效进行。对于大多数这些应用软件来说,已经开发了简单化的软件,但这种方法是一般性的,在这种软件中进行的许多实际计算是相同的。受在原则上可以通过共同代码解决的各种问题的驱使,本文介绍了一种简单的概率化编程语言GaussED,它是一种简单的概率化编程语言,加上一种强大的实验设计引擎,它结合了自动化的顺序式实验设计,以接近(可能非线性)数量对高斯进程模型的兴趣。使用少数命令,高斯ED可以用来:解决线性部分差异方程式,从综合数据中进行地形重建,用梯度数据进行巴耶斯式优化。