# Sequence Analysis and Transition Models

## Détails

ID Serval

serval:BIB_EE6BCEBF29D4

Type

**Partie de livre**

Sous-type

**Chapitre**: chapitre ou section

Collection

Publications

Fonds

Titre

Sequence Analysis and Transition Models

Titre du livre

Encyclopedia of Animal Behavior

Editeur

Elsevier, Academic Press

ISBN

9780128132517

Statut éditorial

Publié

Date de publication

2019

Peer-reviewed

Oui

Editeur scientifique

Choe Jae Chun

Volume

3

Pages

506-517

Edition

2nd ed.

Langue

anglais

Résumé

The behavior of an animal, with or without interaction with its environment, can generally be decomposed into a set of

mutually excluding activity categories such as play, exploration, or rest. Thus, the data consist of one or several series of

successive activities which can be used to answer the following questions: Does the probability of observing a particular

activity depend on the preceding observed activities? Do some particular patterns of successive activities appear more often than expected by chance only? Which external events influence the behavior of this animal?

This article covers several related methods used to answer these questions. At the base is the need to compute and analyze a transition probability, the probability of observing a particular activity in relation to the past. A Markov chain is a stochastic answer to this kind of problem, when lag-sequential analysis refers to a less formalized approach aimed at the identification of the most usual patterns of activities. We review here the basic principles of these two approaches and detail some advances such as the formulation of sequence analysis as log-linear models, the use of mixtures to obtain parsimonious models, the concept of hidden Markov models incorporating latent information, and the use of these models for clustering purpose.

mutually excluding activity categories such as play, exploration, or rest. Thus, the data consist of one or several series of

successive activities which can be used to answer the following questions: Does the probability of observing a particular

activity depend on the preceding observed activities? Do some particular patterns of successive activities appear more often than expected by chance only? Which external events influence the behavior of this animal?

This article covers several related methods used to answer these questions. At the base is the need to compute and analyze a transition probability, the probability of observing a particular activity in relation to the past. A Markov chain is a stochastic answer to this kind of problem, when lag-sequential analysis refers to a less formalized approach aimed at the identification of the most usual patterns of activities. We review here the basic principles of these two approaches and detail some advances such as the formulation of sequence analysis as log-linear models, the use of mixtures to obtain parsimonious models, the concept of hidden Markov models incorporating latent information, and the use of these models for clustering purpose.

Mots-clé

Bayesian information criterion (BIC), Clustering, Double chain markov model (DCMM), Hidden markov model (HMM), Lag-sequential analysis, Log-linear model, Markov chain, Mixture transition distribution (MTD) model, Stable distribution, Time dependence, Transition matrix

Création de la notice

30/01/2019 20:58

Dernière modification de la notice

21/08/2019 5:11