Bayesian Model Posterior Summaries for Coffee Rust Incidence Prediction

Posterior summaries from a Bayesian hierarchical logistic regression model predicting coffee rust incidence. The dataset is a 9.5 KB XLS file authored by Maurice Wanyonyi and last updated on April 21, 2026. It is licensed under CC-BY-4.0 and hosted on figshare.

Use Cases

• Validate Bayesian hierarchical model results based on the posterior summaries.
• Compare parameter estimates for coffee rust incidence prediction based on the model output.
• Incorporate posterior estimates into agricultural risk models based on the statistical results.

Strengths

• Dataset is small (9.5 KB), facilitating quick download and inspection.
• Model outputs are clearly defined as posterior summaries from a Bayesian hierarchical logistic regression.
• License is permissive (CC-BY-4.0), allowing for reuse and modification.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Output from a Bayesian hierarchical logistic regression model.

Time Range

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Freshness

Last updated 2026-04-21 17:43:22; freshness should be verified.

Geography

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