2008/04/09 by Xinjia Chen, Chen, Xinjia · 1 citation
Computer Science · Mathematics · #Applications (stat.AP) #Applied mathematics #Bounded function #Economics #Estimation #FOS: Computer and information sciences #FOS: Mathematics #Mathematical analysis #Mathematical optimization #Mathematics #Probability (math.PR) #Statistical and Computational Modeling #Statistics #Statistics Theory (math.ST) #math.PR #math.ST #stat.AP #stat.TH
paper · pdf · doi:10.48550/arxiv.0804.1399
published in arXiv (Cornell University) (Cornell University) · 10 pages, no figure. Generalized to the case of bounded variables
openalex publication_date 2008/04/09 · arxiv created 2012/12/04 · arxiv updated 2012/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we develop a general approach for probabilistic estimation and optimization. An explicit formula and a computational approach are established for controlling the reliability of probabilistic estimation based on a mixed criterion of absolute and relative errors. By employing the Chernoff-Hoeffding bound and the concept of sampling, the minimization of a probabilistic function is transformed into an optimization problem amenable for gradient descendent algorithms.