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Estimating the standard deviation of soil properties with limited samples through the Bayesian approach
Abstract Characterizing the standard deviation of soil properties is important to a geotechnical probabilistic analysis, and the task is usually achieved with sufficient samples. However, sometimes soil samples or soil tests in a project could be limited (e.g., only one sample), making classical statistics approaches less applicable to the estimating. In this technical note, we introduce a new Bayesian algorithm to estimate the standard deviation of soil properties, using limited project-specific samples along with relevant prior information from the literature. In addition to the methodology, a few demonstrations are also given in the paper, to re-evaluate the standard deviation of soil properties with the new algorithm. Like many Bayesian algorithms, the new application could be useful for site characterizations when samples are limited.
Estimating the standard deviation of soil properties with limited samples through the Bayesian approach
Abstract Characterizing the standard deviation of soil properties is important to a geotechnical probabilistic analysis, and the task is usually achieved with sufficient samples. However, sometimes soil samples or soil tests in a project could be limited (e.g., only one sample), making classical statistics approaches less applicable to the estimating. In this technical note, we introduce a new Bayesian algorithm to estimate the standard deviation of soil properties, using limited project-specific samples along with relevant prior information from the literature. In addition to the methodology, a few demonstrations are also given in the paper, to re-evaluate the standard deviation of soil properties with the new algorithm. Like many Bayesian algorithms, the new application could be useful for site characterizations when samples are limited.
Estimating the standard deviation of soil properties with limited samples through the Bayesian approach
Wang, J. P. (Autor:in) / Xu, Yun (Autor:in)
2014
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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|Approximating the Bayesian estimate of the standard deviation in a linear model
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