The nu expression for probabilistic data integration

DOI10.1007/s11004-007-9117-5zbMath1141.86307OpenAlexW1976212429MaRDI QIDQ937145

Evgenia I. Polyakova, Andre G. Journel

Publication date: 20 August 2008

Published in: Mathematical Geology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11004-007-9117-5

zbMATH Keywords

Data integrationUpdating probabilitiesConditional independenceData interaction vs. dependence

Mathematics Subject Classification ID

Geological problems (86A60)

Related Items (11)

A mathematical view of weights-of-evidence, conditional independence, and logistic regression in terms of Markov random fields ⋮ A multiple training image approach for spatial modeling of geologic domains ⋮ A comparison of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression for mapping mineral prospectivity ⋮ Probability aggregation methods in geoscience ⋮ Fuzzy posterior-probabilistic fusion ⋮ Comparison of mathematical methods of potential modeling ⋮ Entropy-based weighting in one-dimensional multiple errors analysis of geological contacts to model geological structure ⋮ Multiple artificial neural networks with interaction noise for estimation of spatial categorical variables ⋮ DFTopoSim: modeling topographically-controlled deposition of subseismic scale sandstone packages within a mass transport dominated deep-water channel belt ⋮ BoostWofE: a new sequential weights of evidence model reducing the effect of conditional dependency ⋮ Learning Optimal Forecast Aggregation in Partial Evidence Environments

Uses Software

• GSLIB

Cites Work

• The probability perturbation method: a new look at Bayesian inverse modeling
• Combining probability distributions: A critique and an annotated bibliography
• Conditional simulation of complex geological structures using multiple-point statistics
• Combining knowledge from diverse sources: An alternative to traditional data independence hypotheses
• Sampling-Based Approaches to Calculating Marginal Densities
• A Multiplicative Formula for Aggregating Probability Assessments
• Consensus theoretic classification methods

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