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https://hdl.handle.net/2440/86034
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Type: | Journal article |
Title: | A case study of model selection and parameter inference by maximum likelihood with application to uncertainty analysis |
Author: | Pardo-Igúdzquiza, E. Dowd, P.A. |
Citation: | Natural Resources Research, 1998; 7(1):63-73 |
Publisher: | Springer US |
Issue Date: | 1998 |
ISSN: | 1520-7439 1573-8981 |
Statement of Responsibility: | Eulogio Pardo-Igúzquiza, Peter A. Dowd |
Abstract: | One of the uses of geostatistical conditional simulation is as a tool in assessing the spatial uncertainty of inputs to the Monte Carlo method of system uncertainty analysis. Because the number of experimental data in practical applications is limited, the geostatistical parameters used in the simulation are themselves uncertain. The inference of these parameters by maximum likelihood allows for an easy assessment of this estimation uncertainty which, in turn, may be included in the conditional simulation procedure. A case study based on transmissivity data is presented to show the methodology whereby both model selection and parameter inference are solved by maximum likelihood. |
Keywords: | Maximum likelihood transmissivity data Akaike information criterion uncertainty analysis geostatistics |
Rights: | © 1998 International Association for Mathematical Geology |
DOI: | 10.1007/BF02782510 |
Published version: | http://dx.doi.org/10.1007/bf02782510 |
Appears in Collections: | Aurora harvest 2 Civil and Environmental Engineering publications |
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