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dc.contributor.authorSacchi, Giada
dc.contributor.authorSwallow, Ben
dc.identifier.citationSacchi , G & Swallow , B 2021 , ' Toward efficient Bayesian approaches to inference in hierarchical hidden Markov models for inferring animal behavior ' , Frontiers in Ecology and Evolution , vol. 9 , 623731 .
dc.identifier.otherPURE: 283823491
dc.identifier.otherPURE UUID: b1ded5cc-912a-4363-9c30-3b4202bce8b2
dc.identifier.otherScopus: 85106047287
dc.identifier.otherORCID: /0000-0002-0227-2160/work/131588778
dc.description.abstractThe study of animal behavioral states inferred through hidden Markov models and similar state switching models has seen a significant increase in popularity in recent years. The ability to account for varying levels of behavioral scale has become possible through hierarchical hidden Markov models, but additional levels lead to higher complexity and increased correlation between model components. Maximum likelihood approaches to inference using the EM algorithm and direct optimization of likelihoods are more frequently used, with Bayesian approaches being less favored due to computational demands. Given these demands, it is vital that efficient estimation algorithms are developed when Bayesian methods are preferred. We study the use of various approaches to improve convergence times and mixing in Markov chain Monte Carlo methods applied to hierarchical hidden Markov models, including parallel tempering as an inference facilitation mechanism. The method shows promise for analysing complex stochastic models with high levels of correlation between components, but our results show that it requires careful tuning in order to maximize that potential.
dc.relation.ispartofFrontiers in Ecology and Evolutionen
dc.rightsCopyright © 2021 Sacchi and Swallow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.subjectAnimal movementen
dc.subjectBayesian inferenceen
dc.subjectHierarchical hidden Markov modelsen
dc.subjectParallel temperingen
dc.subjectQL Zoologyen
dc.subjectEcology, Evolution, Behavior and Systematicsen
dc.titleToward efficient Bayesian approaches to inference in hierarchical hidden Markov models for inferring animal behavioren
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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