Scholarlink

This work shows how Gaussian Graphical Models (GGMs) varying on a spatial network offer an effective means to accurately portray the conditional dependence relationships among water pollutants observed on a fluvial network. The motivating case study involves evaluating the quality of water bodies in the Piedmont region. In environmental sciences, representing variable relationships through estimated graphs proves both practical and advantageous. After estimating all graphs over a spatial network, it will be possible to identify spatially varying clusters of variables across the fluvial network

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

Spatial Varying Graphical Models for Water Pollutants

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Rosaria Ignaccolo,
  • Nicola Pronello,
  • Alex Cucco,
  • Luigi Ippoliti,
  • Vito Frontuto,
  • Natalia Golini

摘要

This work shows how Gaussian Graphical Models (GGMs) varying on a spatial network offer an effective means to accurately portray the conditional dependence relationships among water pollutants observed on a fluvial network. The motivating case study involves evaluating the quality of water bodies in the Piedmont region. In environmental sciences, representing variable relationships through estimated graphs proves both practical and advantageous. After estimating all graphs over a spatial network, it will be possible to identify spatially varying clusters of variables across the fluvial network