A non-negative expansion for small Jensen-Shannon Divergences

2008/10/28 by A Daniel Raj, Chris H. Wiggins, Raj, Anil +1 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (stat.ML) #Statistical Mechanics and Entropy

paper · pdf · doi:10.48550/arxiv.0810.5117

openalex publication_date 2008/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this report, we derive a non-negative series expansion for the Jensen-Shannon divergence (JSD) between two probability distributions. This series expansion is shown to be useful for numerical calculations of the JSD, when the probability distributions are nearly equal, and for which, consequently, small numerical errors dominate evaluation.

Citations

• Divergence measures based on the Shannon entropy

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