Abstract
AbstractDivergences (distances), which measure the dissimilarity, respectively, proximity, between two probability distributions, have turned out to be very useful for several different tasks in statistics (eg, parameter estimation and goodness‐of‐fit testing), econometrics, machine learning, information theory, etc. Some prominent examples are the Kullback‐Leibler information (relative entropy), the Csiszár‐Ali‐Silvey ϕ‐divergences, the “ordinary” (ie, unscaled) Bregman divergences, and the recently developed more general scaled Bregman divergences. Out of the latter and a novel extension to nonconvex generators, we form a new toolkit for detecting distributional changes in random data (streams and clouds). Some sample‐size asymptotics is investigated as well.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Applied Stochastic Models in Business and Industry
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
