Quantified Maximum Entropy
Quantified Maximum Entropy
复制标题
量化最大熵
DOI：
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发表时间：
1990
期刊：
影响因子：
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通讯作者：
J. Skilling
中科院分区：
文献类型：
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作者：
J. Skilling
这篇教程讨论了量化最大熵的理论基础，作为一种从噪声和不完整数据中获得图像和其他正加性分布的概率估计的技术。分析是完全贝叶斯的，估计总是作为概率分布获得，从中可以找到适当的误差条。这取代了早期的技术，甚至是那些使用最大熵的技术，后者旨在产生单一的最优分布。
This tutorial paper discusses the theoretical basis of quantified maximum entropy, as a technique for obtaining probabilistic estimates of images and other positive additive distributions from noisy and incomplete data. The analysis is fully Bayesian, with estimates always being obtained as probability distributions from which appropriate error bars can be found. This supersedes earlier techniques, even those using maximum entropy, which aimed to produce a single optimal distribution.