Model-based adaptive spatial sampling for occurrence map construction

DOI10.1007/s11222-011-9287-3zbMath1322.62033OpenAlexW2041575310WikidataQ56526301 ScholiaQ56526301MaRDI QIDQ746249

Daniel Spring, Régis Sabbadin, Nathalie Peyrard, Barry Brook, Ralph Mac Nally

Publication date: 16 October 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://hal.inrae.fr/hal-02645082/file/StatComp_1.pdf

zbMATH Keywords

fire ant sampling for mappinghidden Markov random fieldsoptimal sampling approximation

Mathematics Subject Classification ID

Related Items (3)

Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: application to weed map reconstruction ⋮ The value of information for correlated GLMs ⋮ Efficient designs for Bayesian networks with sub-tree bounds

Cites Work

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• EM procedures using mean field-like approximations for Markov model-based image segmentation
• The value of information in spatial decision making
• The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results
• Decision-theoretic optimal sampling in Hidden Markov Random Fiels
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Introduction to Stochastic Search and Optimization
• The complexity of theorem-proving procedures

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