Asymptotic Properties of Bayesian Predictive Densities When the Distributions of Data and Target Variables are Different

[摘要] Bayesian predictive densities when the observed data x and the target variable y to be predicted have different distributions are investigated by using the framework of information geometry. The performance of predictive densities is evaluated by the Kull

[学科分类] 统计和概率

[关键词] differential geometry;Fisher–Rao metric;Jeffreys prior;Kullback–Leibler divergence;predictive metric