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In this context, the consideration of patient heterogeneity in economic evaluations [78] further encourages the use of more complex modeling techniques like DES [79].
It has undergone many developments, one of the important ones being the application of numerical modeling techniques, like the finite element method (FEM) to simulate the process.
In a next step, data modeling techniques like neural nets in a Geographical Information System GISS) can improve the availability of maps and therefore spatial data in our second case study we elaborated a synthetic soil map in the German region Hohenlohe out of various other data like geomorphology and elevation, as well as a reference soil map in an adjacent area.
The statistical concepts discussed by the data analysts ranged from inferential statistics to commonly used modeling techniques, like logistic regression, multiple linear regression, and Cox Proportional Regression modeling.
Therefore, alternative modeling techniques like constraint optimization methods have become more and more popular during the last 15 years.
Subsequently, modeling techniques like decision trees and Markov models are required to model these intermediate outcomes to final outcomes.
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Alternatively, the developed model can be validated through comparing with the results obtained by different modelling techniques like analytical hierarchy process (AHP).
Once the barriers for JIT production are identified, the interaction among the barriers can be analysed using other modelling techniques like ANP or AHP.
However, mutations with small effects are not easily analysed with stoichiometric modelling techniques like flux balance analysis, and wet-lab observations are usually too insensitive for analysing many effects of interest for questions of long-term stability.
Comparison of achieved recognition accuracy with popular modeling techniques in speaker recognition like Gaussian mixture models and vector quantization is made.
Hall (2007) has shown that certain causally different cases may take isomorphic causal models, so that modeling techniques may themselves require something like a default/deviant distinction to discern these cases.
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