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Hidden Markov Models and Animal Behaviour
Author(s) -
Macdonald Iain L.,
Raubenheimer David
Publication year - 1995
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710370606
Subject(s) - markov chain , univariate , variable order markov model , hidden markov model , markov model , multivariate statistics , computer science , markov property , maximum entropy markov model , econometrics , mathematics , statistics , artificial intelligence , machine learning
This paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time‐trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher‐order Markov chain models if a first‐order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time‐trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts ( Locusta migratoria ).