Entropy Approximation

Entropy approximation focuses on efficiently estimating the entropy of complex probability distributions, a crucial task in various fields hampered by computational challenges for high-dimensional data. Current research emphasizes developing faster algorithms, such as those leveraging dimensionality reduction techniques like PCA or stochastic trace approximations, and exploring machine learning regression models to approximate different entropy measures directly from data. These advancements are improving the feasibility of entropy-based analyses in applications ranging from evaluating image irregularity in remote sensing to analyzing information flow in neural networks and diagnosing neurological disorders.