Multievent: An extension of multistate capture-recapture models to uncertain states
Multievent: An extension of multistate capture-recapture models to uncertain states
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DOI：
10.1111/j.1541-0420.2005.00318.x
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发表时间：
2005-06-01
期刊：
影响因子：
1.9
通讯作者：
Pradel, R
中科院分区：
文献类型：
--
作者：
Pradel, R
捕获-再捕获模型最初是为了解释自由放养动物种群中小于1的遭遇概率而开发的。如今，这些模型可以处理动物在不同位置之间的运动，也可以用来研究不同状态之间的转换。然而，它们用于估计状态之间的转换并不考虑状态分配中的不确定性。我提出了多事件模型的扩展，它包含了这种不确定性。多事件模型属于隐马尔可夫模型家族。我还表明，在这篇文章中的记忆模型，其中下一个状态或位置的影响，前一个状态占用，可以完全处理的框架内的多事件模型。
Capture-recapture models were originally developed to account for encounter probabilities that are less than 1 in free-ranging animal populations. Nowadays, these models can deal with the movement of animals between different locations and are also used to study transitions between different states. However, their use to estimate transitions between states does not account for uncertainty in state assignment. I present the extension of multievent models, which does incorporate this uncertainty. Multievent models belong to the family of hidden Markov models. I also show in this article that the memory model, in which the next state or location is influenced by the previous state occupied, can be fully treated within the framework of multievent models.