- Volume 41 Issue 2
DOI QR Code
A Methodology for Estimating the Uncertainty in Model Parameters Applying the Robust Bayesian Inferences
- Kim, Joo Yeon (Korean Association for Radiation Application) ;
- Lee, Seung Hyun (Korean Association for Radiation Application) ;
- Park, Tai Jin (Korean Association for Radiation Application)
- Received : 2015.07.17
- Accepted : 2016.06.13
- Published : 2016.06.30
Background: Any real application of Bayesian inference must acknowledge that both prior distribution and likelihood function have only been specified as more or less convenient approximations to whatever the analyzer's true belief might be. If the inferences from the Bayesian analysis are to be trusted, it is important to determine that they are robust to such variations of prior and likelihood as might also be consistent with the analyzer's stated beliefs. Materials and Methods: The robust Bayesian inference was applied to atmospheric dispersion assessment using Gaussian plume model. The scopes of contaminations were specified as the uncertainties of distribution type and parametric variability. The probabilistic distribution of model parameters was assumed to be contaminated as the symmetric unimodal and unimodal distributions. The distribution of the sector-averaged relative concentrations was then calculated by applying the contaminated priors to the model parameters. Results and Discussion: The sector-averaged concentrations for stability class were compared by applying the symmetric unimodal and unimodal priors, respectively, as the contaminated one based on the class of
Supported by : Korea Institute of Energy Technology Evaluation and Planning (KETEP)
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