Premium
Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States
Author(s) -
Pradel Roger
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00318.x
Subject(s) - mark and recapture , extension (predicate logic) , computer science , hidden markov model , state (computer science) , markov chain , markov model , econometrics , markov process , mathematics , algorithm , statistics , artificial intelligence , machine learning , population , demography , sociology , programming language
Summary 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.