2014/04/05 by Dangna Li, Kun Yang, Li, Dangna +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #Statistical Methods and Inference
paper · doi:10.48550/arxiv.1404.1425
openalex publication_date 2014/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
observations from an unknown absolute continuous distribution defined on some domain Ω, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function. Our density estimate is a piecewise constant function defined on a binary partition of Ω. The key ingredient of the algorithm is to use discrepancy, a concept originates from Quasi Monte Carlo analysis, to control the partition process. The resulting algorithm is simple, efficient, and has a provable convergence rate. We empirically demonstrate its efficiency as a density estimation method. We also show how it can be utilized to find good initializations for k-means.