JSD

JSD is an acronym that most commonly refers to the Jensen-Shannon divergence in information theory and statistics. The Jensen-Shannon divergence (JSD) is a method for measuring the similarity between two probability distributions.

For discrete distributions P and Q over the same domain, and M = (P+Q)/2, JSD(P||Q) = (1/2) KL(P||M) + (1/2) KL(Q||M). The KL terms are the Kullback-Leibler divergences. The JSD is symmetric, non-negative, and finite (assuming certain support conditions) and is typically bounded by 0 and 1 when logarithms base 2 are used.

Because it smooths and averages the distributions, it is less sensitive to zero probabilities than KL divergence. It is widely used in machine learning, natural language processing, clustering, anomaly detection, and bioinformatics to quantify distribution similarity, compare models, or evaluate text corpora.

JSD also serves as an acronym in other domains. In computing, it may be used as shorthand for JavaScript development or debugging in some projects, though it is not a standardized term. In publishing and organizations, JSD can denote journals or groups whose initials match JSD. Context is required to determine the intended meaning.