This review examines the relevance of parameter identifiability for statistical models used in
machine learning. In addition to defining main concepts, we address several issues of
identifiability closely related to machine learning, showing the advantages and
disadvantages of state-of-the-art research and demonstrating recent progress. First, we
review criteria for determining the parameter structure of models from the literature. This has
three related issues: parameter identifiability, parameter redundancy, and …