We define a new class of Gaussian processes on compact metric graphs such as street or river networks. The proposed models, the Whittle--Mat\'ern fields, are defined via a fractional stochastic differential equation on the compact metric graph and are a natural extension of Gaussian fields with Mat\'ern covariance functions on Euclidean domains to the non-Euclidean metric graph setting. Existence of the processes, as well as some of their main properties, such as sample path regularity are derived. The model class in particular contains differentiable processes. To the best of our knowledge, this is the first construction of a differentiable Gaussian process on general compact metric graphs. Further, we prove an intrinsic property of these processes: that they do not change upon addition or removal of vertices with degree two. Finally, we obtain Karhunen--Lo\`eve expansions of the processes, provide numerical experiments, and compare them to Gaussian processes with isotropic covariance functions.
翻译:我们在紧凑的度量图,例如街道或河流网络上定义了一类新的高斯过程。所提出的模型,即Whittle-Matérn场,通过度量图上的分数随机微分方程定义,是欧几里得域上具有Matérn协方差函数的高斯场在非欧几里得度量图设置中的自然扩展。推导了过程的存在性以及一些主要性质,如样本路径的正则性。该模型类特别包含可微分过程。据我们所知,这是一般紧凑度量图上不可微高斯过程的首次构造。此外,我们证明了这些过程的内在特性:它们不会因增加或删除度数为2的顶点而改变。最后,我们获得了过程的Karhunen-Loève展开,并提供了数值实验,并将其与具有各向同性协方差函数的高斯过程进行了比较。