Estimation of mutual information using kernel density estimators

1995/09/01 by Young-Il Moon, Young‐Il Moon, Balaji Rajagopalan +1 · 7 citations
Computer Science · Economics, Econometrics and Finance · Neuroscience · #Complex Systems and Time Series Analysis #Neural dynamics and brain function #Nonlinear Dynamics and Pattern Formation

paper · doi:10.1103/physreve.52.2318

openalex publication_date 1995/09/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/29

Abstract

Mutual information is useful for investigating the dependence between two experimental time series. It is often used to establish an appropriate time delay in phase-portrait reconstruction from time-series data. A histogram based approach has been used so far to estimate the probabilities. It is shown here that kernel density estimation of the probability density functions needed in estimating the average mutual information across two coordinates can be more effective than the histogram method of Fraser and Swinney [Phys. Rev. A 33, 1134 (1986)].

Citations

• Deterministic Nonperiodic Flow

Cited by

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• k-NN Estimation of Directed Information
• Mutual Information as a Figure of Merit for Optical Fiber Systems
• Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

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