Model-based methods are approaches in various fields, such as statistics, machine learning, and engineering, that rely on mathematical models to represent complex systems. These models help in understanding, predicting, and optimizing the behavior of the system by simulating different scenarios and analyzing outcomes. In machine learning, model-based methods often involve creating a model of the data, which can then be used to make predictions or decisions. This contrasts with model-free methods, which learn directly from data without an explicit model. Model-based approaches can be more efficient and effective, especially when data is limited or costly to obtain.