Choosing a Suitable Prior for Bayesian Learning Based on Bayesian Discrimination
贝叶斯学习中基于贝叶斯判别分析的先验分布选取

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.