Rényi's entropy

Rényi’s entropy is a measure of the uncertainty or randomness in a set of data, similar to the more familiar Shannon entropy. It provides a way to quantify how unpredictable a system is, but with a parameter that allows focusing on different aspects of the data’s variability. For example, it can emphasize rare or common events depending on the setting. Rényi’s entropy is useful in fields like information theory, physics, and machine learning for analyzing complex systems or signals, offering a flexible tool to understand the diversity and complexity within data.