Bayesian analysis of linear dominance hierarchies
Bayesian analysis of linear dominance hierarchies
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DOI：
10.1016/j.anbehav.2004.08.011
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发表时间：
2005-05-01
期刊：
影响因子：
2.5
通讯作者：
Adams, ES
中科院分区：
文献类型：
--
作者：
Adams, ES
对群居动物的研究通常试图确定统治等级，其中根据成对遭遇中的输赢次数根据竞争能力对个体进行排名。我说明贝叶斯方法，配对比较的方法的基础上，确定排名和估计优势能力和其他属性之间的关系。贝叶斯推断将每个未知参数的先验概率分布与似然函数相结合，以产生感兴趣的量的联合后验概率分布。与推断等级的非参数技术相反，贝叶斯模型为每个推断产生确定性的测量，并且即使等级本身存在相当大的不确定性，也允许严格估计等级和协变量之间的相关性。对贝叶斯方法的一个可能的反对意见是，它似乎比简单的方法需要更多的限制性假设。然而，模拟结果表明，贝叶斯推断是更强大的偏离这些假设比非参数方法的结果。(c)2005年，动物行为研究协会。由爱思唯尔有限公司出版。保留所有权利。
Studies on social animals often seek to identify dominance hierarchies, in which individuals are ranked according to competitive abilities based on counts of wins and losses in pairwise encounters. I illustrate Bayesian approaches, based on the method of paired comparisons, for determining ranks and for estimating relationships between dominance ability and other attributes. Bayesian inference combines prior probability distributions for each unknown parameter with likelihood functions to produce the joint posterior probability distribution for the quantities of interest. In contrast to nonparametric techniques for inferring ranks, Bayesian models yield measures of certainty for each inference and allow rigorous estimates of correlations between ranks and covariates even when there is considerable uncertainty as to the ranks themselves. A possible objection to the Bayesian approach is that it appears to entail more restrictive assumptions than do simpler methods. However, simulations show that Bayesian inferences are more robust to deviations from these assumptions than are the results of nonparametric methods. (c) 2005 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.