Abstract
A novel histogram-based mutual information estimator using data-driven tree-structured partitions (TSP) is presented in this work. The TSP is the solution of a complexity regularized empirical information maximization (EIM) criterion, with the objective to find a good tradeoff between the known estimation and approximation errors. We show that this solution is density-free strongly consistent and, furthermore, it provides a near-optimal balance between the mentioned variance-bias errors.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
