Divergence Measures
Divergence measures are mathematical functions used in statistics, machine learning, and information theory to quantify the difference or dissimilarity between two probability distributions. They provide a way to compare how one distribution diverges from another, with applications in model evaluation, clustering, and optimization. Common examples include Kullback-Leibler divergence, Jensen-Shannon divergence, and Wasserstein distance.
Developers should learn divergence measures when working on machine learning projects involving probabilistic models, such as variational autoencoders, generative adversarial networks, or Bayesian inference, to assess model performance and similarity. They are also useful in data analysis tasks like clustering, anomaly detection, and information retrieval, where measuring distribution differences is critical for accuracy and efficiency.