Potential modeling: conditional independence matters

DOI10.1007/s13137-014-0059-zzbMath1305.62236OpenAlexW2075105471MaRDI QIDQ2254029

Helmut Schaeben

Publication date: 4 February 2015

Published in: GEM - International Journal on Geomathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13137-014-0059-z

zbMATH Keywords

logistic regressionartificial neural netsweights-of-evidencecompositional regressionimbalanced training datasetnaïve Bayes modelsignificance of models

Mathematics Subject Classification ID

General nonlinear regression (62J02)

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Uses Software

• compositions
• R

Cites Work

• Support vector machines with applications
• New perspectives on water chemistry and compositional data analysis
• A mathematical view of weights-of-evidence, conditional independence, and logistic regression in terms of Markov random fields
• A comparison of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression for mapping mineral prospectivity
• MODELING THE SPATIAL DISTRIBUTION OF MINERAL DEPOSITS USING NEURAL NETWORKS
• Statistical Interpretation of Species Composition
• Analyzing Compositional Data with R
• The elements of statistical learning. Data mining, inference, and prediction
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