A TMB implementation of habitat-driven Langevin diffusion with Laplace approximation integrates over latent true locations to enable single-stage inference of resource selection from irregular error-prone tracking data.
Th´ eo Michelot, Richard Glennie, Catriona Harris, and Len Thomas
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Adding a drift term that enforces known spatial boundaries to an underdamped Langevin movement model improves the accuracy of Kalman and particle filters applied to noisy animal telemetry data.
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Inferring resource selection and utilization distributions from irregular and error-prone animal tracking data
A TMB implementation of habitat-driven Langevin diffusion with Laplace approximation integrates over latent true locations to enable single-stage inference of resource selection from irregular error-prone tracking data.
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Spatial constraints improve filtering of measurement noise from animal tracks
Adding a drift term that enforces known spatial boundaries to an underdamped Langevin movement model improves the accuracy of Kalman and particle filters applied to noisy animal telemetry data.