Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study

This study empirically tested the robustness of Bayesian Maximum Entropy (BME) in predicting spatiotemporal data, with an emphasis on skewness, sample size, and spatial dependency level. Simulated data, both Gaussian and non-Gaussian, were generated using the unconditional sequential simulation meth...

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Bibliographic Details
Published in:Scientific reports 2024-11, Vol.14 (1), p.28214-8, Article 28214
Main Authors: Gongnet, Emmanuel Ehnon, Agbangba, Codjo Emile, Affossogbe, Sèdjro A Tranquillin, Vihotogbé, Romaric, Glèlè Kakaï, Romain
Format: Article
Language:English
Subjects:
631/114
639/705
Accuracy
Bayesian analysis
Entropy
Humanities and Social Sciences
Knowledge
Maximum entropy
multidisciplinary
Partial differential equations
Sample size
Science
Science (multidisciplinary)
Simulation
Skewness
Soil sciences
Variables
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Summary:This study empirically tested the robustness of Bayesian Maximum Entropy (BME) in predicting spatiotemporal data, with an emphasis on skewness, sample size, and spatial dependency level. Simulated data, both Gaussian and non-Gaussian, were generated using the unconditional sequential simulation method, with sample sizes ranging from 100 to 500 at the interval length of 50 and varying skewness (0, 1, 3, 6 and 9) and spatial dependency levels (weak, moderate, and strong). Findings revealed sample size variations and spatial dependence levels did not significantly influence BME prediction’s Mean Square Error (MSE) and bias. While skewness significantly impacted MSE (p-value 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-70518-z