Joint Distributions

Joint distributions describe how two or more random variables behave together, not only individually. They capture the complete relationship between variables, including any dependencies or correlations.

In machine learning, understanding joint distributions is crucial for multivariate analysis, feature engineering, and building models that capture complex relationships between variables. Many algorithms implicitly or explicitly model joint distributions to make accurate predictions across multiple dimensions of data.