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Following Barr et al. (2013), we used a model comparison approach.
The segmentation method combines a model comparison approach with a multiscale fusion and a neighbor combination process.
Equations are derived for the proposed Bayesian model comparison approach, in which the inherent spatial variability is modeled explicitly using random field theory.
Multiple regression analysis and an information-theoretic model comparison approach were used to identify field and landscape parameters that account for variation in damage.
In this paper a model comparison approach based on material flow systems is investigated that is divided into a microscopic and a macroscopic model scale.
We tested the proposed model by means of longitudinal structural equation modeling in a 9-year four-wave panel design, by applying a model comparison approach and indirect effect analyses (N = 608 professionals).
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Until recently, Bayesian model comparison approaches such as Bayes factors have largely been ignored in analysing the identifiability of biological systems from time series data [ 14].
Existing data-model comparison approaches either cannot correctly detect changed objects packed in small spaces, or cannot handle the computational complexity of comparing detailed as-designed and as-built geometries of MEP systems that contain hundreds or even thousands of elements (e.g., ducts).
For the three-population model, a round-robin comparison approach was implemented.
For testing the reliability of the proposed model, comparison with other phenomenological approaches has been made.
A second approach to model comparison is to penalize model complexity (models that have more parameters).
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Justyna Jupowicz-Kozak
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