Bayesian inference for continuous-time step-and-turn movement models with noisy observations. PhD thesis: 2014-2018.
Paul Blackwell (supervisor) This PhD thesis concerns the statistical modelling of animal movement paths given observed GPS locations. With observations being in discrete time, mechanistic models of movement are often formulated as such. This popularity remains despite an inability to compare analyses through scale invariance and common problems handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow, often excused by a difficulty in interpreting the `instantaneous’ parameters associated with a continuous-time model.