dos Santos, Antonia Alessandra Lemos
(2023)
Latent Tensor Bayesian Models for Estimating
Complex Interactions in Plant Variety Testing.
PhD thesis, National University of Ireland Maynooth.
Abstract
This thesis addresses the critical challenge of predicting crop yield. As the demand
for food surges due to population growth, accurate and efficient predictive models
become increasingly important. Leveraging a comprehensive database from
the Horizon 2020 InnoVar project, which includes variables across phenomics, genomics,
soil, and weather conditions, we aim to extend the existing Additive Main
Effects and Multiplicative Interaction (AMMI) modelling framework.
Our work is divided into three key contributions. First, we propose a computationally
efficient Bayesian AMMI model using variational inference, addressing the
high computational costs often associated with traditional Markov chain Monte
Carlo methods. Second, we introduce the Bayesian Additive Main effects and Multiplicative
Interaction Tensor (BAMMIT) model, which extends the AMMI model
to accommodate multiple categorical variables. Third, we present the Clustered
Bayesian Additive Main Effects and Multiplicative Interaction Tensor (CBAMMIT)
model, incorporating Gaussian Mixture Models to allow for the inclusion of
categorical representations of numerical variables.
Our findings show that these extensions not only improve predictive accuracy
but also offer probabilistic assessments of predictions. They have real-world applicability,
as demonstrated using data from Ireland, and can potentially guide
stakeholders in agriculture – from farmers to policymakers – in making informed
decisions.
| Item Type: |
Thesis
(PhD)
|
| Keywords: |
Latent Tensor Bayesian Models; Estimating
Complex Interactions; Plant Variety Testing; |
| Academic Unit: |
Faculty of Science and Engineering > Mathematics and Statistics |
| Item ID: |
19045 |
| Depositing User: |
IR eTheses
|
| Date Deposited: |
15 Oct 2024 11:32 |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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