Improved inference for stochastic movement models

This project looks at ways to improve the application of continuous-time movement models, such as multi-state diffusion processes. It will look at improved Markov chain Monte Carlo algorithms that exploit the structure of the models better than existing algorithms, and at approximations that limit the complexity of the state-switching process.