Quantization of Prior Probabilities for Hypothesis Testing
Quantization of Prior Probabilities for Hypothesis Testing
复制标题
用于假设检验的先验概率的量化
DOI：
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发表时间：
2008
影响因子：
5.4
通讯作者：
L. Varshney
中科院分区：
文献类型：
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作者：
Kush R. Varshney;L. Varshney
本文研究了假设的先验概率为随机向量时的贝叶斯假设检验问题。最近邻和质心的条件，使用平均贝叶斯风险误差（MBRE）作为量化失真的措施。失真率函数的高分辨率近似也得到了。研究了隔离群体中的人类决策问题，假设贝叶斯假设检验与量化先验。
In this paper, Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error (MBRE) as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors.