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State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They...
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| Pubblicato in: | Sci Rep |
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| Autori principali: | , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Publishing Group
2016
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4879567/ https://ncbi.nlm.nih.gov/pubmed/27220686 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/srep26677 |
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