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Stratified Adaptive Cluster Sampling
Biometrika, 1991SUMMARY Stratified adaptive cluster sampling refers to designs in which, following an initial stratified sample, additional units are added to the sample from the neighbourhood of any selected unit with an observed value that satisfies a condition of interest.
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Adaptive modeling, adaptive data assimilation and adaptive sampling
Physica D: Nonlinear Phenomena, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage
Adaptive Agents and Multi-Agent Systems, 2019We consider the problem of online distributed environmental modeling and adaptive sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena ...
Wenhao Luo +3 more
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Journal of the American Statistical Association, 1990
Abstract In many real-world sampling situations, researchers would like to be able to adaptively increase sampling effort in the vicinity of observed values that are high or otherwise interesting. This article describes sampling designs in which, whenever an observed value of a selected unit satisfies a condition of interest, additional units are added
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Abstract In many real-world sampling situations, researchers would like to be able to adaptively increase sampling effort in the vicinity of observed values that are high or otherwise interesting. This article describes sampling designs in which, whenever an observed value of a selected unit satisfies a condition of interest, additional units are added
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2007 46th IEEE Conference on Decision and Control, 2007
There has recently been considerable attention devoted to sample-based approaches to chance constraints in stochastic programming, and also multi-stage optimization formulations. In this short paper, we consider the merits of a joint approach. A specific motivation for us, is the possibility of developing techniques suitable for integer-constrained ...
Dimitris Bertsimas +1 more
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There has recently been considerable attention devoted to sample-based approaches to chance constraints in stochastic programming, and also multi-stage optimization formulations. In this short paper, we consider the merits of a joint approach. A specific motivation for us, is the possibility of developing techniques suitable for integer-constrained ...
Dimitris Bertsimas +1 more
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2012
One of the main methods of adaptive sampling is adaptive cluster sampling. As it involves unequal probability of sampling, standard Horvitz-Thompson and Hansen-Hurwitz estimators can be modified to provide unbiased estimates of finite population parameters along with unbiased variance estimators.
George A. F. Seber, Mohammad M. Salehi
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One of the main methods of adaptive sampling is adaptive cluster sampling. As it involves unequal probability of sampling, standard Horvitz-Thompson and Hansen-Hurwitz estimators can be modified to provide unbiased estimates of finite population parameters along with unbiased variance estimators.
George A. F. Seber, Mohammad M. Salehi
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Adaptive play with spatial sampling
Games and Economic Behavior, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Durieu, Jacques, Solal, Philippe
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IEEE Transactions on Automatic Control, 1969
An adaptive sampling technique utilizing the first three terms of a Taylor series expansion about the n th sampling instant for the error signal in an error-sampled feedback control system is presented. The necessary rules for determining the sign convention used in applying the technique are given along with an example illustrating the method.
J. Mitchell, W. McDaniel
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An adaptive sampling technique utilizing the first three terms of a Taylor series expansion about the n th sampling instant for the error signal in an error-sampled feedback control system is presented. The necessary rules for determining the sign convention used in applying the technique are given along with an example illustrating the method.
J. Mitchell, W. McDaniel
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Computing, 1990
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