Identifiability analysis

Identifiability analysis is a concept primarily used in the fields of statistics, machine learning, and system identification. It refers to the ability to determine unique model parameters from the observed data. In other words, a model is said to be identifiable if different parameter values lead to different probability distributions of the observed data. ### Key Aspects of Identifiability Analysis 1. **Model Parameters**: The analysis focuses on determining whether the parameters of a model can be uniquely estimated given the observed data.