In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyse individual animal movement data. Specifically, we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement.
In AStA, 2017

Methodology is presented for Markov chain Monte Carlo inference given irregular observations of a multi-state, continuous-time movement process, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to well-known GPS data from elk (Cervus elaphus), which have previously been modelled in discrete time.
In JABES, 2017

A method for Bayesian inference for single-state, continuous-time step-and-turn movement models is described through the use of a Markov chain Monte Carlo approach. Analysis on real data on an individual reindeer (Rangifer tarandus) illustrates the presented methods.
In BAYSM 2016, 2017


This is a quick comparison of simple logistic regression fitting in R and julia. Spoiler: julia is faster.


Teaching and Outreach


  • Lecture cover (5 lectures) for ‘Bayesian Statistics’ course (levels 3, 4, MSc. including distance learners).
  • Lectured and assessed ‘Probability in the Law’, a short course (5 lectures) at level 2.
  • Tutorial demonstration and marking for probability and statistics courses at levels 1-3 in the mathematics department.
  • Tutorial demonstration for mathematics at level 1 in the chemistry department.
  • Tutorial demonstration for python at level 1 in the computer science department and for python, R and LaTeX at level 1 in the mathematics department.


  • Code Club (Lydgate Lane Primary School ages 9-11).
  • PopMaths yearly events (Sheffield Hallam University, ages 13-18).
  • Exploring STEM for girls (University of Sheffield, ages 14-16).