Euclidean Information Theory
Euclidean Information Theory
复制标题
欧几里得信息论
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Lizhong Zheng
中科院分区：
文献类型：
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作者：
Shashi Borade;Lizhong Zheng
信息论中的许多问题都涉及优化概率分布之间的 Kullback-Leibler (KL) 散度。由于 KL 散度很难分析，因此这些优化通常很棘手。我们通过假设感兴趣的分布彼此接近来简化这些问题。在此假设下，KL 散度的行为类似于欧氏距离的平方。通过这种简化，我们解决了使用降级消息集进行广播的开放问题，作为网络信息论问题的典型示例。
Many problems in information theory involve optimizing the Kullback-Leibler (KL) divergence between probability distributions. Since KL divergence is difficult to analyze, these optimizations are often intractable. We simplify these problems by assuming the distributions of interest to be close to each other. Under this assumption, the KL divergence behaves like a squared Euclidean distance. With this simplification, we solve the open problem of broadcasting with degraded message sets, as a canonical example of network information theory problems.