首页 | 本学科首页   官方微博 | 高级检索  
     


Improved Poisson intensity estimation: denoising application using Poisson data
Authors:Lu   H. Kim   Y. Anderson   J.M.M.
Affiliation:Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
Abstract:Recently, Timmermann and Nowak developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data.
Keywords:
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号