Scholarlink

<p>This work presents a deep Gaussian process model for spatial interpolation. The model consists of an irregular network where each node is a Gaussian process whose output is a full Gaussian distribution, as opposed to the single value provided by standard deep neural networks. Each node integrates the uncertainty in its parent nodes analytically through the use of a specialized non-stationary covariance function. The network is capable of modelling multiple variables, even of different types, and its construction allows the imputation of external knowledge, such as lithologically constrained grades and correlations. A special node based on stochastic differential equations learns non-stationary patterns automatically by warping the input space. The model is applied to a time series and two- and three-dimensional cases. An open-source implementation in Python is available.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Uncertainty Propagation in Deep Gaussian Process Networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月7日

查看全文
  • Ítalo Gomes Gonçalves,
  • Florian Wellmann

摘要

This work presents a deep Gaussian process model for spatial interpolation. The model consists of an irregular network where each node is a Gaussian process whose output is a full Gaussian distribution, as opposed to the single value provided by standard deep neural networks. Each node integrates the uncertainty in its parent nodes analytically through the use of a specialized non-stationary covariance function. The network is capable of modelling multiple variables, even of different types, and its construction allows the imputation of external knowledge, such as lithologically constrained grades and correlations. A special node based on stochastic differential equations learns non-stationary patterns automatically by warping the input space. The model is applied to a time series and two- and three-dimensional cases. An open-source implementation in Python is available.