# Sequence Analysis and Transition Models

## Details

Serval ID

serval:BIB_EE6BCEBF29D4

Type

**A part of a book**

Publication sub-type

**Chapter**: chapter ou part

Collection

Publications

Fund

Title

Sequence Analysis and Transition Models

Title of the book

Encyclopedia of Animal Behavior

Publisher

Elsevier, Academic Press

ISBN

9780128132517

Publication state

Published

Issued date

2019

Peer-reviewed

Oui

Editor

Choe Jae Chun

Volume

3

Pages

506-517

Edition

2nd ed.

Language

english

Abstract

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.

Keywords

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

Create date

30/01/2019 21:58

Last modification date

21/08/2019 6:11