An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference

2018/06/27 by Vianey Leos‐Barajas, Théo Michelot, Leos-Barajas, Vianey +1 · 1 citation
Computer Science · Environmental Science · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Biological sciences #FOS: Computer and information sciences #Music and Audio Processing #Quantitative Methods (q-bio.QM) #Wildlife Ecology and Conservation

paper · pdf · doi:10.48550/arxiv.1806.10639

openalex publication_date 2018/06/27 · openalex created_date 2018/07/10 · openalex updated_date 2026/07/28

Abstract

Hidden Markov models (HMMs) are popular time series model in many fields including ecology, economics and genetics. HMMs can be defined over discrete or continuous time, though here we only cover the former. In the field of movement ecology in particular, HMMs have become a popular tool for the analysis of movement data because of their ability to connect observed movement data to an underlying latent process, generally interpreted as the animal's unobserved behavior. Further, we model the tendency to persist in a given behavior over time. Notation presented here will generally follow the format of Zucchini et al. (2016) and cover HMMs applied in an unsupervised case to animal movement data, specifically positional data. We provide Stan code to analyze movement data of the wild haggis as presented first in Michelot et al. (2016).

Cited by

• Extending finite mixture models with skew-normal distributions and hidden Markov models for time series

Related

• Scale dependence in hidden Markov models for animal movement
• Multi-scale modeling of animal movement and general behavior data using\n hidden Markov models with hierarchical structures
• Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
• Inference on state occupancy in covariate-driven hidden Markov models
• Improved order selection method for hidden Markov models: a case study with movement data