Estimation of Mutual Information

Focusing on mutual information (MI) estimation methods, the book delves into innovative approaches for learning forest structures from multivariate data and conducting independent component analysis. It defines MI in the context of information theory, highlighting its role in measuring dependencies between random variables. The author emphasizes the significance of consistent estimations that satisfy independence testing, a combination not previously achieved in existing methods. This work is crucial for applications in machine learning, statistical analysis, and various scientific fields.