%0 Journal Article
%T Choosing a Suitable Prior for Bayesian Learning Based on Bayesian Discrimination<br>贝叶斯学习中基于贝叶斯判别分析的先验分布选取
%A HU Zhen-Yu LIN Shi-Min LU Yu-Chang
%A <br>胡振宇
%A 林士敏
%A 陆玉昌
%J 计算机科学
%D 2003
%I 
%X In this paper we propose an experimental method to choose a prior distribution. Different from many researchers, who offered lots of principles that separated from sample information, we consider it a Bayesian discrimination problem combining with the sample information. We introduce the concept of Posterior belief about prior distributions. With the well-known Bayes theorem we give out a formula to calculate it and propose a method to discriminate a prior between prior distributions- Highest Posterior Belief (HPB). We also show that under certain condition, the HPB method is identical with the ML-II method.
%K Machine learning
%K Prior distribution
%K Bayesian discrimination
%K Prior belief
%K Posterior belief<br>贝叶斯学习
%K 贝叶斯判别分析
%K 先验分布
%K 概率
%K 先验信念比
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=EC1AC87FED7C45CC&yid=D43C4A19B2EE3C0A&vid=340AC2BF8E7AB4FD&iid=5D311CA918CA9A03&sid=03A030BB0C519C60&eid=5E25104E99903E8A&journal_id=1002-137X&journal_name=计算机科学&referenced_num=2&reference_num=5