Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: application to weed map reconstruction

DOI10.1016/j.csda.2013.10.002zbMath1506.62024OpenAlexW1985133095WikidataQ60558398 ScholiaQ60558398MaRDI QIDQ1623384

Régis Sabbadin, Nathalie Peyrard, Sabrina Gaba, Mathieu Bonneau

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.10.002

zbMATH Keywords

dynamic programmingMarkov decision processsampling designGibbs samplingleast-squares linear regressionweed mapping

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to environmental and related topics (62P12) Sampling theory, sample surveys (62D05) Learning and adaptive systems in artificial intelligence (68T05) Markov and semi-Markov decision processes (90C40)

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