%0 Journal Article %A HU Zhenyu %A LIN Shimin %T Linear opinion pool prior for Bayesian learning %D 2012 %R %J Computer Engineering and Applications %P 33-35 %V 48 %N 1 %X This paper brings forward a new technique for prior choosing in Bayesian learning, in which several priors are averaged in weight to form the Linear Opinion Pool(LOP), and then compound parameters are chosen to get an approximation of suitable prior. This paper also proves the equivalency between the likelihood of LOP prior and the likelihood of the compound parameters, and offers a method of MLE or moment to determine the compound parameters, therefore a suitable LOP prior is determined. In this way one can use sample data to correct known prior, derive undiscovered and reasonable prior, therefore can make Bayesian learning more effective.
%U http://cea.ceaj.org/EN/abstract/article_27493.shtml