ABSTRACT
When modeling sometimes multiple models (and parameters) are used to describe a context or situation. Information criteria are applied in these contexts to assess the best approximating model, which should support other statistical approaches to model evaluation. This chapter introduces two types of information criteria (e.g. Akaike Information Criterion and Bayesian Information Criterion) for model assessment and evaluation.
