摘要
In this paper we propose an experimental method to choose a prior distribution. Different from many re-searchers, who offered lots of principles that separated from sample information, we consider it a Bayesian discrimina-tion problem combining with the sample information. We introduce the concept of Posterior belief about prior distri-butions. With the well-known Bayes theorem we give out a formula to calculate it and propose a method to discrirni-nate a prior between prior distributions-- Highest Posterior Belief (HPB). We also show that under certain condition,the HPB method is identical with the ML-I method.
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.
出处
《计算机科学》
CSCD
北大核心
2003年第8期134-135,共2页
Computer Science
基金
智能技术与系统国家重点实验室开放课题(99002)
关键词
贝叶斯学习
贝叶斯判别分析
先验分布
概率
先验信念比
Machine learning, Prior distribution, Bayesian discrimination, Prior belief, Posterior belief
