Philip MaybankPhD Student in Statistics with Richard Everitt in collaboration with Ingo Bojak
Dept. of Mathematics and Statistics, University of Reading
Room 202, JJT Building
This is my academic web page, containing a summary of the various things I did before my PhD, and information on my current research in Statistical Modelling and Monte Carlo methods.
I am currently looking at statistical inference for stochastic dynamical systems models with unknown parameters. I am focusing mechanistic models of brain activity that describe the interaction between excitatory and inhibitory neurons in the cortex, for example, the Wilson-Cowan network oscillator, and the Liley model for the effect of anaesthesia. The aims of my research are to develop computationally efficient MCMC methods for parameter estimation in such models, particularly in the case where extra-cortical input is a stochastic process. And, I also aim to use these models to make inferences from EEG recordings, e.g. inferring depth of anaesthesia.
I have also looked at at multivariate statistical models that contain an intractable normalisation term, such as Markov random fields and exponential random graphs. The aims of my research are, (i) developing computationally efficient MCMC methods for parameter estimation in such models, (ii) exploring whether these models are useful for inferring brain connectivity from brain imaging data.
Publications (from before PhD)
Google Scholar Profile
Conferences / Summer Schools / Workshops
Experience and Education