[HTML][HTML] Learning Bayesian networks with heterogeneous agronomic data sets via mixed-effect models and hierarchical clustering

L Valleggi, M Scutari, FM Stefanini - Engineering Applications of Artificial …, 2024 - Elsevier
Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan
Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statistical …

Learning Bayesian networks with heterogeneous agronomic data sets via mixed-effect models and hierarchical clustering

L Valleggi, M Scutari, FM Stefanini - … APPLICATIONS OF ARTIFICIAL …, 2024 - air.unimi.it
Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan
Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statistical …

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering

L Vallegi, M Scutari, FM Stefanini - arXiv preprint arXiv:2308.06399, 2023 - arxiv.org
Research involving diverse but related data sets, where associations between covariates
and outcomes may vary, is prevalent in various fields including agronomic studies. In these …

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering

L Vallegi, M Scutari, F Mattia Stefanini - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
Research involving diverse but related data sets, where associations between covariates
and outcomes may vary, is prevalent in various fields including agronomic studies. In these …

[PDF][PDF] Learning Bayesian networks with heterogeneous agronomic data sets via mixed-effect models and hierarchical clustering

L Valleggi, M Scutari, FM Stefanini - Engineering Applications of Artificial …, 2024 - air.unimi.it
Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan
Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statistical …