mellanmodellsosäkerheter
Mellanmodellsosäkerheter refers to uncertainties that arise from the process of model selection and specification when building statistical or machine learning models. This concept is particularly relevant in Bayesian modeling, where uncertainty quantification is a core principle. These uncertainties stem from the fact that there is often no single, uniquely correct model to describe a given phenomenon. Researchers and practitioners must make choices about the functional form of relationships, the distributions of variables, and the inclusion or exclusion of certain predictors. Each of these choices introduces a degree of uncertainty.
The difficulty in addressing mellanmodellsosäkerheter lies in the fact that these are not simply parameter uncertainties
Methods to address mellanmodellsosäkerheter include using model averaging techniques, where predictions or inferences are made by