Bayesian analyses for a multiple capture-recapture model
Bayesian analyses for a multiple capture-recapture model
复制标题
多重捕获-再捕获模型的贝叶斯分析
DOI：
10.1093/biomet/78.2.399
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发表时间：
1991
期刊：
影响因子：
2.7
通讯作者：
Philip J. Smith
中科院分区：
文献类型：
--
作者：
Philip J. Smith
摘要在多次捕获-再捕获调查中，捕获的概率在不同的采样场合可能会有所不同。解释这种变化的模型称为At。给出了总体规模N的区间估计、决策和点估计问题的Bayes、经验Bayes和Bayes经验Bayes解。当采样次数为小到中等，并且在每次采样时观察到的回收单位数量为中等时，从经验贝叶斯和贝叶斯经验贝叶斯方法获得的估计值与使用捕获概率参考先验分布的贝叶斯方法非常接近。然而，当采样次数很多，而在每个采样次数上观察到的捕获单元数量很少时，使用不同的参考先验获得的推断可能会有很大差异。
SUMMARY In multiple capture-recapture surveys, the probability of capture can vary between sampling occasions. The model accounting for this variation is known as At. Bayes, empirical Bayes, and Bayes empirical Bayes solutions are given to the problems of interval estimation, decision making, and point estimation of the population size N. When the number of sampling occasions is small to moderate and the number of recaptured units observed on each sampling occasion is moderate, estimates obtained from empirical Bayes and Bayes empirical Bayes methods compare closely to Bayesian methods using a reference prior distribution for the capture probabilities. However, when the number of sampling occasions is large and the number of recaptured units observed on each sampling occasion is small, inferences obtained using different reference priors can differ considerably.