n样本高斯状态的贝叶斯预测
Bayesian prediction of the Gaussian states from n sample
摘要：
近年来，量子预测问题在贝叶斯框架下被提出。结果表明，当我们用基于先验的平均相对熵评价贝叶斯预测密度算子时，贝叶斯预测密度算子是最好的预测密度算子，作为一个说明性的例子，我们把采用高斯分布的高斯态族作为先验，给出了外差测量固定的贝叶斯预测密度算子.通过计算每个平均相对熵，我们证明了它优于基于极大似然估计的插入式预测密度算子。
英文摘要：
Recently quantum prediction problem was proposed in the Bayesian framework. It is shown that Bayesian predictive density operators are the best predictive density operators when we evaluate them by using the average relative entropy based on a prior.As an illustrative example, we treat the Gaussian states family adopting the Gaussian distribution as a prior and give the Bayesian predictive density operator with the heterodyne measurement fixed. We show that it is better than the plug-in predictive density operator based on the maximum likelihood estimate by calculating each average relative entropy.
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