Bayesian prediction of the Gaussian states from n sample
Bayesian prediction of the Gaussian states from n sample
复制标题
n 个样本的高斯状态的贝叶斯预测
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
F. Komaki
中科院分区：
文献类型：
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作者：
F. Tanaka;F. Komaki
近年来，量子预测问题在贝叶斯框架下被提出。结果表明，当我们用平均相对熵对贝叶斯预测密度算子进行评价时，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。我们通过计算每个平均相对熵，证明了它优于基于最大似然估计的插件预测密度算子。
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 this http URL 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.