Toward efficient Bayesian approaches to inference in hierarchical hidden Markov models for inferring animal behavior
Date
05/05/2021Keywords
Metadata
Show full item recordAltmetrics Handle Statistics
Altmetrics DOI Statistics
Abstract
The 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.
Citation
Sacchi , 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 . https://doi.org/10.3389/fevo.2021.623731
Publication
Frontiers in Ecology and Evolution
Status
Peer reviewed
ISSN
2296-701XType
Journal article
Rights
Copyright © 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.
Collections
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.