ロード中...
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geos...
保存先:
| 出版年: | J Am Stat Assoc |
|---|---|
| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2016
|
| 主題: | |
| オンライン・アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5927603/ https://ncbi.nlm.nih.gov/pubmed/29720777 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2015.1044091 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|