Bayesian approaches for adaptive spatial sampling : an example application.

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BAASS (Bayesian Approaches for Adaptive Spatial Sampling) is a set of computational routines developed to support the design and deployment of spatial sampling programs for delineating contamination footprints, such as those that might result from the accidental or intentional environmental release of radionuclides. BAASS presumes the existence of real-time measurement technologies that provide information quickly enough to affect the progress of data collection. This technical memorandum describes the application of BAASS to a simple example, compares the performance of a BAASS-based program with that of a traditional gridded program, and explores the significance of several of the underlying assumptions required ... continued below

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Johnson, R. L.; LePoire, D.; Huttenga, A. & Quinn, J. May 25, 2005.

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This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 11 times , with 8 in the last month . More information about this report can be viewed below.

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Description

BAASS (Bayesian Approaches for Adaptive Spatial Sampling) is a set of computational routines developed to support the design and deployment of spatial sampling programs for delineating contamination footprints, such as those that might result from the accidental or intentional environmental release of radionuclides. BAASS presumes the existence of real-time measurement technologies that provide information quickly enough to affect the progress of data collection. This technical memorandum describes the application of BAASS to a simple example, compares the performance of a BAASS-based program with that of a traditional gridded program, and explores the significance of several of the underlying assumptions required by BAASS. These assumptions include the range of spatial autocorrelation present, the value of prior information, the confidence level required for decision making, and ''inside-out'' versus ''outside-in'' sampling strategies. In the context of the example, adaptive sampling combined with prior information significantly reduced the number of samples required to delineate the contamination footprint.

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  • Report No.: ANL/EAD/TM/05-1
  • Grant Number: W-31-109-ENG-38
  • DOI: 10.2172/881581 | External Link
  • Office of Scientific & Technical Information Report Number: 881581
  • Archival Resource Key: ark:/67531/metadc886185

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  • May 25, 2005

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Feb. 17, 2017, 1:06 p.m.

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Johnson, R. L.; LePoire, D.; Huttenga, A. & Quinn, J. Bayesian approaches for adaptive spatial sampling : an example application., report, May 25, 2005; United States. (digital.library.unt.edu/ark:/67531/metadc886185/: accessed October 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.