Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement
Abstract
We discuss the notorious problem of order selection in hidden Markov models, that is of selecting an adequate number of states, highlighting typical pitfalls and practical challenges arising when analyzing real data. Extensive simulations are used to demonstrate the reasons that render order selection particularly challenging in practice despite the conceptual simplicity of the task. In particular, we demonstrate why well-established formal procedures for model selection, such as those based on standard information criteria, tend to favor models with numbers of states that are undesirably large in situations where states shall be meaningful entities. We also offer a pragmatic step-by-step approach together with comprehensive advice for how practitioners can implement order selection. Our proposed strategy is illustrated with a real-data case study on muskox movement.Supplementary materials accompanying this paper appear online.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
For animal behavior data, formal model selection techniques tend to select more HMM states than seem biologically reasonable.
“For animal behavior data, formal model selection techniques tend to select more HMM states than seem biologically reasonable (i.e., additional states compensate for the inability to capture all the structure in such complex data); therefore, we first considered a simple (2-state) model and then built up complexity until a reasonable goodness-of-fit was achieved, but such that the model states were”
From Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands (2020) · cites this paper as [72]
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793 · see citing works
Model Selection and Multimodel Inference
(2003) · Technometrics · doi:10.1198/tech.2003.s146 · Cited by 5,505 · see citing works
Assessing a mixture model for clustering with the integrated completed likelihood
Christophe Biernacki, A., Celeux, G., & Govaert, G. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.865189 · Cited by 1,537 · see citing works
An asymptotic equivalence of choice of model by cross validation and Akaike's criterion
Stone, M. (1977) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1977.tb01603.x · Cited by 1,286 · see citing works
EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS
Morales, J. M., Haydon, D. T., Frair, J. L., Holsinger, K. E., & Fryxell, J. M. (2004) · Ecology · doi:10.1890/03-0269 · Cited by 801 · see citing works
Hidden Markov Models for Time Series: An Introduction Using R
Zucchini, W., & MacDonald, I. L. (2009) · Thammasat University Digital Collections · Cited by 686 · see citing works
Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States
Pradel, R. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00318.x · Cited by 494 · see citing works
An Introduction to Model Selection
Zucchini, W. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1999.1276 · Cited by 466 · see citing works
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions
Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., & Morales, J. M. (2012) · Ecology · doi:10.1890/11-2241.1 · Cited by 465 · see citing works
Self-citationMichelot, T., Langrock, R., & Patterson, T. A. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12578 · Cited by 440 · see citing works
Self-citationClassifying movement behaviour in relation to environmental conditions using hidden Markov models
Patterson, T. A., Basson, M., Bravington, M. V., & Gunn, J. (2009) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2009.01583.x · Cited by 335 · see citing works
Hennig, C. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.04.009 · Cited by 282 · see citing works
Robert, C. P., Rydèn, T., & Titterington, D. M. (2000) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00219 · Cited by 205 · see citing works
Analysis of animal accelerometer data using hidden Markov models
Leos‐Barajas, V., Photopoulou, T., Langrock, R., Patterson, T. A., Watanabe, Y. Y., Murgatroyd, M., & Papastamatiou, Y. P. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12657 · Cited by 187 · see citing works
Self-citationSelecting hidden Markov model state number with cross-validated likelihood
Celeux, G., & Durand, J.-B. (2007) · Computational Statistics · doi:10.1007/s00180-007-0097-1 · Cited by 161 · see citing works
Sex‐specific and individual preferences for hunting strategies in white sharks
Towner, A. V., Barajas, V. L., Langrock, R., Schick, R. S., Smale, M. J., Kaschke, T., Jewell, O. J. D., & Papastamatiou, Y. P. (2015) · Functional Ecology · doi:10.1111/1365-2435.12613 · Cited by 120 · see citing works
Self-citationA multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure
DeRuiter, S. L., Langrock, R., Skirbutas, T., Goldbogen, J. A., Calambokidis, J., Friedlaender, A. S., & Southall, B. L. (2017) · The Annals of Applied Statistics · doi:10.1214/16-aoas1008 · Cited by 105 · see citing works
Self-citationNonparametric Inference in Hidden Markov Models Using P-Splines
Langrock, R., Kneib, T., Silbersdorff, A., & DeRuiter, S. L. (2015) · Biometrics · doi:10.1111/biom.12282 · Cited by 68 · see citing works
Self-citationSchmidt, N. M., van Beest, F. M., Mosbacher, J. B., Stelvig, M., Hansen, L. H., Nabe‐Nielsen, J., & Gr⊘ndahl, C. (2016) · Wildlife Biology · doi:10.2981/wlb.00219 · Cited by 62 · see citing works
Self-citationOptimal hunting conditions drive circalunar behavior of a diurnal carnivore
Broekhuis, F., Grünewälder, S., McNutt, J., & Macdonald, D. (2014) · Behavioral Ecology · doi:10.1093/beheco/aru122 · Cited by 56 · see citing works
Incorporating periodic variability in hidden Markov models for animal movement
Li, M., & Bolker, B. M. (2017) · Movement Ecology · doi:10.1186/s40462-016-0093-6 · Cited by 50 · see citing works
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition
Zucchini, W., MacDonald, I. L., & Langrock, R. (2021) · Cited by 43 · see citing works
Self-citationLangrock, R., Marques, T. A., Baird, R. W., & Thomas, L. (2013) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-013-0158-6 · Cited by 39 · see citing works
Self-citationFlexible Latent‐State Modelling of Old Faithful's Eruption Inter‐Arrival Times in 2009
Langrock, R. (2012) · Australian & New Zealand Journal of Statistics · doi:10.1111/j.1467-842x.2012.00669.x · Cited by 13 · see citing works
Self-citationStatistical modelling of animal movement: a myopic review and a discussion of good practicePreprint
Patterson, T. A., Parton, A., Langrock, R., Blackwell, P. G., Thomas, L., & King, R. (2016) · arXiv (Cornell University) · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
1 of the 27 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Sensitivity and specificity of information criteria
(2019) · doi:10.1093/bib/bbz016 · Cited by 718 · see citing works
Uncovering ecological state dynamics with hidden Markov models
McClintock, B. T., Langrock, R., Giménez, O., Cam, E., Borchers, D. L., Glennie, R., & Patterson, T. A. (2020) · Ecology Letters · doi:10.1111/ele.13610 · Cited by 229 · see citing works
Self-citationGrecian, W. J., Lane, J. V., Michelot, T., Wade, H. M., & Hamer, K. C. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0084 · Cited by 107 · see citing works
Hidden Markov models: Pitfalls and opportunities in ecology
Glennie, R., Adam, T., Barajas, V. L., Michelot, T., Photopoulou, T., & McClintock, B. T. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13801 · Cited by 102 · see citing works
Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models
Adam, T., Griffiths, C. A., Barajas, V. L., Meese, E. N., Lowe, C. G., Blackwell, P. G., Righton, D. A., & Langrock, R. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13241 · Cited by 83 · see citing works
Self-citationPatin, R., Étienne, M., Lebarbier, É., Chamaillé‐Jammes, S., & Benhamou, S. (2019) · Journal of Animal Ecology · doi:10.1111/1365-2656.13105 · Cited by 80 · see citing works
State‐switching continuous‐time correlated random walks
Michelot, T., & Blackwell, P. G. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13154 · Cited by 79 · see citing works
Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories
Marzuki, M. I., Gaspar, P., Garello, R., Kerbaol, V., & Fablet, R. (2017) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2017.2723278 · Cited by 68 · see citing works
McClintock, B. T., London, J. M., Cameron, M. F., & Boveng, P. L. (2017) · Ecosphere · doi:10.1002/ecs2.1751 · Cited by 68 · see citing works
Sex‐specific effects of wind on the flight decisions of a sexually dimorphic soaring bird
Clay, T. A., Joo, R., Weimerskirch, H., Phillips, R. A., den Ouden, O. F. C., Basille, M., Clusella‐Trullas, S., Assink, J. D., & Patrick, S. C. (2020) · Journal of Animal Ecology · doi:10.1111/1365-2656.13267 · Cited by 73 · see citing works
Behavioral state resource selection in invasive wild pigs in the Southeastern United States
Clontz, L. M., Pepin, K. M., VerCauteren, K. C., & Beasley, J. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-86363-3 · Cited by 59 · see citing works
Chimienti, M., Desforges, J.-P. W., Beumer, L. T., Nabe‐Nielsen, J., van Beest, F. M., & Schmidt, N. M. (2020) · Ecological Modelling · doi:10.1016/j.ecolmodel.2020.109250 · Cited by 57 · see citing works
Self-citationConners, M. G., Michelot, T., Heywood, E. I., Orben, R. A., Phillips, R. A., Vyssotski, A. L., Shaffer, S. A., & Thorne, L. H. (2021) · Movement Ecology · doi:10.1186/s40462-021-00243-z · Cited by 56 · see citing works
Flexible hidden Markov models for behaviour-dependent habitat selection
Klappstein, N. J., Thomas, L., & Michelot, T. (2023) · Movement Ecology · doi:10.1186/s40462-023-00392-3 · Cited by 53 · see citing works
Effects of a seismic survey on movement of free-ranging Atlantic cod
van der Knaap, I., Reubens, J., Thomas, L., Ainslie, M. A., Winter, H. V., Hubert, J., Martin, B. S., & Slabbekoorn, H. (2021) · Current Biology · doi:10.1016/j.cub.2021.01.050 · Cited by 53 · see citing works
Bacheler, N., Michelot, T., Cheshire, R. T., & Shertzer, K. (2019) · Fisheries Research · doi:10.1016/j.fishres.2019.02.014 · Cited by 38 · see citing works
Schwarz, J. F. L., Mews, S., DeRango, E. J., Langrock, R., Piedrahita, P. M., Páez‐Rosas, D., & Krüger, O. (2021) · Oecologia · doi:10.1007/s00442-021-04850-w · Cited by 46 · see citing works
Self-citationMcClintock, B. T. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13619 · Cited by 45 · see citing works
Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images
Charfi, S., Ansari, M. E., & Balasingham, I. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6232 · Cited by 42 · see citing works
Beumer, L. T., Pohle, J., Schmidt, N. M., Chimienti, M., Desforges, J.-P. W., Hansen, L. H., Langrock, R., Pedersen, S. H., Stelvig, M., & van Beest, F. M. (2020) · Movement Ecology · doi:10.1186/s40462-020-00213-x · Cited by 40 · see citing works
Self-citationBray, B. C., & Dziak, J. J. (2018) · Learning and Individual Differences · doi:10.1016/j.lindif.2018.06.001 · Cited by 37 · see citing works
Dunphy, B. J., Vickers, S., Zhang, J., Sagar, R. L., Landers, T. J., Bury, S. J., Hickey, A. J., & Rayner, M. J. (2020) · Marine Biology · doi:10.1007/s00227-020-3672-4 · Cited by 36 · see citing works
Maruotti, A., Punzo, A., & Bagnato, L. (2018) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nby019 · Cited by 35 · see citing works
Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies
Maruotti, A., & Punzo, A. (2021) · International Statistical Review · doi:10.1111/insr.12436 · Cited by 35 · see citing works
Understanding decision making in a food-caching predator using hidden Markov models
Farhadinia, M. S., Michelot, T., Johnson, P. J., Hunter, L., & Macdonald, D. W. (2020) · Movement Ecology · doi:10.1186/s40462-020-0195-z · Cited by 34 · see citing works
Chimienti, M., van Beest, F. M., Beumer, L. T., Desforges, J.-P. W., Hansen, L. H., Stelvig, M., & Schmidt, N. M. (2021) · Ecosphere · doi:10.1002/ecs2.3565 · Cited by 34 · see citing works
Self-citationA copula-based multivariate hidden Markov model for modelling momentum in football
Ötting, M., Langrock, R., & Maruotti, A. (2021) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-021-00395-8 · Cited by 33 · see citing works
Self-citationA hierarchical path-segmentation movement ecology framework
Getz, W. M. (2022) · Ecological Processes · doi:10.1186/s13717-022-00399-5 · Cited by 31 · see citing works
Cardiec, F., Bertrand, S., Witt, M. J., Metcalfe, K., Godley, B. J., McClellan, C., Vilela, R., Parnell, R. J., & Loc’h, F. L. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234091 · Cited by 31 · see citing works
Kock, A. A., Lombard, A. T., Daly, R., Goodall, V., Meÿer, M. A., Johnson, R. L., Fischer, C., Koen, P., Irion, D. T., Gennari, E., et al. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.811985 · Cited by 30 · see citing works
Zhang, J., Rayner, M. J., Vickers, S., Landers, T. J., Sagar, R. L., Stewart, J., & Dunphy, B. J. (2019) · Emu - Austral Ornithology · doi:10.1080/01584197.2018.1558997 · Cited by 29 · see citing works
The hidden brain-state dynamics of tACS aftereffects
Kasten, F. H., & Herrmann, C. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119713 · Cited by 29 · see citing works
Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek
Bagi, B., Brecht, M., & Sanguinetti-Scheck, J. I. (2022) · Current Biology · doi:10.1016/j.cub.2022.04.068 · Cited by 27 · see citing works
Spline‐based nonparametric inference in general state‐switching models
Langrock, R., Adam, T., Barajas, V. L., Mews, S., Miller, D. L., & Papastamatiou, Y. P. (2018) · Statistica Neerlandica · doi:10.1111/stan.12133 · Cited by 27 · see citing works
Self-citationIncorporating movement patterns to discern habitat selection: black bears as a case study
Karelus, D. L., McCown, J. W., Scheick, B. K., van de Kerk, M., Bolker, B. M., & Oli, M. K. (2019) · Wildlife Research · doi:10.1071/wr17151 · Cited by 27 · see citing works
Caution is warranted when using animal space-use and movement to infer behavioral states
Buderman, F. E., Gingery, T. M., Diefenbach, D. R., Gigliotti, L. C., Begley-Miller, D., McDill, M. E., Wallingford, B. D., Rosenberry, C. S., & Drohan, P. J. (2021) · Movement Ecology · doi:10.1186/s40462-021-00264-8 · Cited by 27 · see citing works
Activity modulation and selection for forests help giant anteaters to cope with temperature changes
Giroux, A., Ortega, Z., Attias, N., Desbiez, A. L. J., do Valle, D. R., Börger, L., & Oliveira‐Santos, L. G. R. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.04.008 · Cited by 27 · see citing works
Ngô, M. C., Heide‐Jørgensen, M. P., & Ditlevsen, S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006425 · Cited by 26 · see citing works
Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method
Cullen, J. A., Poli, C. L., Fletcher, R. J., & do Valle, D. R. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13745 · Cited by 26 · see citing works
Dynamic-Parameter Movement Models Reveal Drivers of Migratory Pace in a Soaring Bird
Eisaguirre, J. M., Auger‐Méthé, M., Barger, C. P., Lewis, S. B., Booms, T. L., & Breed, G. A. (2019) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2019.00317 · Cited by 26 · see citing works
Effects of 2D Seismic on Snow Crab Movement Behavior
Côté, D., Morris, C. J., Regular, P. M., & Piersiak, M. G. (2020) · Fisheries Research · doi:10.1016/j.fishres.2020.105661 · Cited by 25 · see citing works
Pascalis, F. D., Pála, D., Pisu, D., Morinay, J., Benvenuti, A., Spano, C., Ruiu, A., Serra, L., Rubolini, D., & Cecere, J. G. (2021) · Marine Ecology Progress Series · doi:10.3354/meps13726 · Cited by 25 · see citing works
Evaluating the constraints governing activity patterns of a coastal marine top predator
Byrnes, E. E., Daly, R., Barajas, V. L., Langrock, R., & Gleiss, A. C. (2021) · Marine Biology · doi:10.1007/s00227-020-03803-w · Cited by 23 · see citing works
Self-citationEzaki, T., Himeno, Y., Watanabe, T., & Masuda, N. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15386 · Cited by 23 · see citing works
Syrjälä, E., Nevalainen, J., Peltonen, J., Takkinen, H., Hakola, L., Erlund, I., Veijola, R., Ilonen, J., Toppari, J., Knip, M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-44196-1 · Cited by 22 · see citing works
Hidden semi-Markov-switching quantile regression for time series
Maruotti, A., Petrella, L., & Sposito, L. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107208 · Cited by 21 · see citing works
Automatic selection of the number of clusters using Bayesian clustering and sparsity‐inducing priors
do Valle, D. R., Jameel, Y., Betancourt, B., Azeria, E. T., Attias, N., & Cullen, J. A. (2021) · Ecological Applications · doi:10.1002/eap.2524 · Cited by 21 · see citing works
Quantile hidden semi-Markov models for multivariate time series
Merlo, L., Maruotti, A., Petrella, L., & Punzo, A. (2022) · Statistics and Computing · doi:10.1007/s11222-022-10130-1 · Cited by 21 · see citing works
Crabs ride the tide: incoming tides promote foraging of Giant Mud Crab (Scylla serrata)
Hewitt, D. E., Johnson, D. D., Suthers, I. M., & Taylor, M. D. (2023) · Movement Ecology · doi:10.1186/s40462-023-00384-3 · Cited by 21 · see citing works
Hubert, J., Campbell, J., & Slabbekoorn, H. (2020) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2020.111680 · Cited by 20 · see citing works
Integrated Population Models: Achieving Their Potential
Frost, F., McCrea, R. S., King, R., Giménez, O., & Zipkin, E. F. (2022) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-022-00302-7 · Cited by 24 · see citing works
Ryu, J., Sükei, E., Norbury, A., Liu, S. H., Campaña-Montes, J. J., Baca‐García, E., Artés-Rodrı́guez, A., & Pérez-Rodríguez, M. M. (2021) · JMIR Mental Health · doi:10.2196/30833 · Cited by 19 · see citing works
Football tracking data: a copula-based hidden Markov model for classification of tactics in football
Ötting, M., & Karlis, D. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04660-0 · Cited by 21 · see citing works
Drivers of polar bear behavior and the possible effects of prey availability on foraging strategy
Togunov, R. R., Derocher, A. E., Lunn, N. J., & Auger‐Méthé, M. (2022) · Movement Ecology · doi:10.1186/s40462-022-00351-4 · Cited by 19 · see citing works
Beetles on the move: Not‐just‐a‐technical review of beetles' radio‐tracking
Růžičková, J., & Elek, Z. (2022) · Entomologia Experimentalis et Applicata · doi:10.1111/eea.13260 · Cited by 19 · see citing works
How to account for behavioral states in step-selection analysis: a model comparison
Pohle, J., Signer, J., Eccard, J. A., Dammhahn, M., & Schlägel, U. E. (2024) · PeerJ · doi:10.7717/peerj.16509 · Cited by 19 · see citing works
Self-citationA Time-Series Model for Underdispersed or Overdispersed Counts
MacDonald, I. L., & Bhamani, F. (2018) · The American Statistician · doi:10.1080/00031305.2018.1505656 · Cited by 18 · see citing works
Modelling multi‐scale, state‐switching functional data with hidden Markov models
Sidrow, E., Heckman, N., Fortune, S. M. E., Trites, A. W., Murphy, I., & Auger‐Méthé, M. (2021) · Canadian Journal of Statistics · doi:10.1002/cjs.11673 · Cited by 17 · see citing works
Validating Corruption Risk Measures: A Key Step to Monitoring SDG Progress
Gnaldi, M., & Sarto, S. D. (2023) · Social Indicators Research · doi:10.1007/s11205-023-03238-y · Cited by 17 · see citing works
Ali, S., & Bouguila, N. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111594 · Cited by 17 · see citing works
Kabbaj, O. A., Péan, L.-M., Masson, J.-B., Marhic, B., & Delahoche, L. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112985 · Cited by 17 · see citing works
Lawler, E., Whoriskey, K., Aeberhard, W. H., Field, C., & Flemming, J. M. (2019) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-019-00366-2 · Cited by 16 · see citing works
Nagel, R., Mews, S., Adam, T., Stainfield, C., Fox‐Clarke, C., Toscani, C., Langrock, R., Forcada, J., & Hoffman, J. I. (2021) · Scientific Reports · doi:10.1038/s41598-021-93253-1 · Cited by 16 · see citing works
Self-citationBehavioural responses of fin whales to military mid-frequency active sonar
Southall, B. L., Allen, A. N., Calambokidis, J., Casey, C. B., DeRuiter, S. L., Fregosi, S., Friedlaender, A. S., Goldbogen, J. A., Harris, C. M., Hazen, E. L., et al. (2023) · Royal Society Open Science · doi:10.1098/rsos.231775 · Cited by 16 · see citing works
A guide to state–space modeling of ecological time series
Auger‐Méthé, M., Newman, K. B., Cole, D. J., Empacher, F., Gryba, R., King, A. A., Barajas, V. L., Flemming, J. M., Nielsen, A., Petris, G., et al. (2021) · Ecological Monographs · doi:10.1002/ecm.1470 · Cited by 291 · see citing works
Maurer, N., Boyi, J. O., Schick, L. A., Nachtsheim, D. A., Schaffeld, T., Groß, S., Teilmann, J., Schnitzler, J., & Siebert, U. (2025) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2025.117777 · Cited by 15 · see citing works
A framework for integrating inferred movement behavior into disease risk models
Dougherty, E. R., Seidel, D. P., Blackburn, J. K., Turner, W. C., & Getz, W. M. (2022) · Movement Ecology · doi:10.1186/s40462-022-00331-8 · Cited by 15 · see citing works
Agachi, E., Bijmolt, T. H. A., van Ittersum, K., & Mierau, J. O. (2023) · JMIR mhealth and uhealth · doi:10.2196/43033 · Cited by 15 · see citing works
Penalized estimation of flexible hidden Markov models for time series of counts
Adam, T., Langrock, R., & Weiß, C. H. (2019) · METRON · doi:10.1007/s40300-019-00153-6 · Cited by 15 · see citing works
Self-citationPacket Loss Characterization Using Cross Layer Information and HMM for Wi-Fi Networks
da Silva, C. A. G., & Pedroso, C. M. (2022) · Sensors · doi:10.3390/s22228592 · Cited by 14 · see citing works
Clay, T. A., Hodum, P. J., Hagen, E. N., & Brooke, M. D. L. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.02.007 · Cited by 14 · see citing works
A hidden Markov model for continuous longitudinal data with missing responses and dropout
Pandolfi, S., Bartolucci, F., & Pennoni, F. (2023) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.202200016 · Cited by 14 · see citing works
Finding the number of latent states in hidden Markov models using information criteria
Buckby, J., Wang, T., Fletcher, D., Zhuang, J., Takeo, A., & Obara, K. (2023) · Environmental and Ecological Statistics · doi:10.1007/s10651-023-00584-5 · Cited by 13 · see citing works
Anomaly Detection in Smart Industrial Machinery Through Hidden Markov Models and Autoencoders
Sorostinean, R., Burghelea, Z., & Gellert, A. (2024) · IEEE Access · doi:10.1109/access.2024.3400970 · Cited by 7 · see citing works
Bayesian non-parametric detection heterogeneity in ecological models
Turek, D., Wehrhahn, C., & Giménez, O. (2021) · Environmental and Ecological Statistics · doi:10.1007/s10651-021-00489-1 · Cited by 13 · see citing works
Emergence of the wrapped Cauchy distribution in mixed directional data
Bailey, J. D., & Codling, E. A. (2020) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-020-00380-7 · Cited by 13 · see citing works
Threat-Event Detection for Distributed Networks Based on Spatiotemporal Markov Random Field
Ma, H., Xie, Y., Tang, S., Hu, J., & Liu, X. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3036664 · Cited by 12 · see citing works
Flexible Modelling of Diel and Other Periodic Variation in Hidden Markov Models
Feldmann, C. C., Mews, S., Coculla, A., Stanewsky, R., & Langrock, R. (2023) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-023-00342-7 · Cited by 12 · see citing works
Self-citationA regularized hidden Markov model for analyzing the ‘hot shoe’ in football
Ötting, M., & Groll, A. (2021) · Statistical Modelling · doi:10.1177/1471082x211008014 · Cited by 12 · see citing works
A probabilistic framework for predicting disease dynamics: A case study of psychotic depression
Bueno, M. L. P., Hommersom, A., Lucas, P., & Janzing, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103232 · Cited by 12 · see citing works
Using accelerometry tags to quantify gray whale foraging behavior
Colson, K. M., Pirotta, E., New, L., Cade, D. E., Calambokidis, J., Bierlich, K. C., Bird, C. N., Ajó, A. F., Hildebrand, L., Trites, A. W., et al. (2024) · Marine Mammal Science · doi:10.1111/mms.13210 · Cited by 11 · see citing works
Milles, A., Dammhahn, M., Jeltsch, F., Schlägel, U. E., & Grimm, V. (2021) · The American Naturalist · doi:10.1086/718473 · Cited by 11 · see citing works
Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging
Hu, M., McComb, C., & Goucher-Lambert, K. (2023) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060423000021 · Cited by 11 · see citing works
Raboin, M. J., Plumb, J. M., Sholtis, M. D., Smith, D. L., Jackson, P. R., Rivera, J. M., Suski, C. D., & Cupp, A. R. (2023) · Movement Ecology · doi:10.1186/s40462-023-00396-z · Cited by 11 · see citing works
Hambuckers, J., Kneib, T., Langrock, R., & Silbersdorff, A. (2018) · Quantitative Finance · doi:10.1080/14697688.2017.1417625 · Cited by 11 · see citing works
Self-citationGardiner, R. Z., Hamer, R. P., Barajas, V. L., Peñaherrera-Palma, C. R., Jones, M. E., & Johnson, C. N. (2019) · Ecology and Evolution · doi:10.1002/ece3.5519 · Cited by 11 · see citing works
Diving deeper into the underlying white shark behaviors at Guadalupe Island, Mexico
Aquino‐Baleytó, M., Barajas, V. L., Adam, T., Hoyos-Padilla, E. M., Santana‐Morales, O., Galván‐Magaña, F., González‐Armas, R., Lowe, C. G., Ketchum, J. T., & Villalobos, H. (2021) · Ecology and Evolution · doi:10.1002/ece3.8178 · Cited by 10 · see citing works
Bacri, T., Berentsen, G. D., Bulla, J., & Hølleland, S. (2022) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.202100256 · Cited by 12 · see citing works
Cullen, J. A., Attias, N., Desbiez, A. L. J., & do Valle, D. R. (2023) · PeerJ · doi:10.7717/peerj.14726 · Cited by 10 · see citing works
A multilevel latent Markov model for the evaluation of nursing homes' performance
Montanari, G. E., Doretti, M., & Bartolucci, F. (2018) · Biometrical Journal · doi:10.1002/bimj.201700223 · Cited by 10 · see citing works
Gradient boosting in Markov-switching generalized additive models for location, scale, and shape
Adam, T., Mayr, A. J., & Kneib, T. (2021) · Econometrics and Statistics · doi:10.1016/j.ecosta.2021.04.002 · Cited by 10 · see citing works
Bayesian inference for continuous-time hidden Markov models with an unknown number of states
Luo, Y., & Stephens, D. A. (2021) · Statistics and Computing · doi:10.1007/s11222-021-10032-8 · Cited by 10 · see citing works
Flexible hidden Markov models for behaviour-dependent habitat selectionPreprint
Klappstein, N. J., Thomas, L., & Michelot, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.30.518554 · Cited by 10 · see citing works
Characterising eye movement events with an unsupervised hidden markov model
Lüken, M., Kucharský, Š., & Visser, I. (2022) · Journal of Eye Movement Research · doi:10.16910/jemr.15.1.4 · Cited by 9 · see citing works
Gennari, E., Irion, D. T., & Cowley, P. D. (2022) · Animal Biotelemetry · doi:10.1186/s40317-022-00295-x · Cited by 9 · see citing works
Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data
Moraga, S. M., & Aarts, E. (2023) · Multivariate Behavioral Research · doi:10.1080/00273171.2023.2205392 · Cited by 9 · see citing works
Linear and landscape disturbances alter Mojave desert tortoise movement behavior
Hromada, S. J., Esque, T. C., Vandergast, A. G., Drake, K. K., Chen, F., Gottsacker, B. O., Swart, J., & Nussear, K. E. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.971337 · Cited by 9 · see citing works
Larochelambert, Q. D., Hamri, I., Chassard, T., Meignié, A., Storme, F., Dupuit, M., Diry, A., Toussaint, J., Louis, P., Coulmy, N., et al. (2024) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2023-001810 · Cited by 9 · see citing works
Using Hidden Markov Models to characterise intermittent social behaviour in fish shoals
Bode, N. W. F., & Seitz, M. J. (2017) · Die Naturwissenschaften · doi:10.1007/s00114-017-1534-9 · Cited by 9 · see citing works
Maximum approximate likelihood estimation of general continuous-time state-space models
Mews, S., Langrock, R., Ötting, M., Yaqine, H., & Reinecke, J. (2022) · Statistical Modelling · doi:10.1177/1471082x211065785 · Cited by 9 · see citing works
Self-citation
- Authors
- Pohle, J., Langrock, R., van Beest, F. M., & Schmidt, N. M.
- Affiliations
- Bielefeld University (DE)
- Aarhus University (DK)
- Corresponding author
- Pohle, Jennifer
- Venue
- Journal of Agricultural, Biological and Environmental Statistics
- Published
- 2017-06-05
- Volume
- 22
- Issue
- 3
- Pages
- 270-293
- ISSN
- 1085-7117 (ISSN-L)
- 1537-2693
- OpenAlex
- W2622738426
- Cited by
- 288
- References
- 27
- Citations by year
- 2026: 29 · 2025: 37 · 2024: 38 · 2023: 40 · 2022: 43 · 2021: 43 · 2020: 22 · 2019: 21 · 2018: 10 · 2017: 4 · 2012: 1
- FWCI
- 9.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Agricultural Biological and Environmental Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Scopus (Elsevier) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Keywords
- hidden Markov models, model order selection, number of states, model selection, information criteria, animal movement, Ovibos moschatus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review