Dr Theodore Papamarkou
Theodore's research focuses on the development of statistical inference for differential equations driven by rough paths. Previous research interests and related publications of Theodore have spanned variance reduction for Hamiltonian Monte Carlo, Bayesian model selection and MCMC inference for in silico pathway reconstruction in systems biology, non-linear time series and non-linear dynamics, statistical modelling for the optimization of performance in chaos communications, statistical genetics and epigenetic approaches to cardiometabolic risk factors.
Non-Linear Dynamics of Trajectories Generated by Fully-Stretching Piece-Wise Linear Maps. T. Papamarkou & A. J. Lawrance. International Journal of Bifurcation and Chaos, 24(5):1450071/1-1450071/11, (2014).
Zero Variance Differential Geometric Markov Chain Monte Carlo Algorithms. T. Papamarkou, A. Mira & M. Girolami. Bayesian Analysis, 9(1):97-128, (2014).
Paired Bernoulli Circular Spreading: Attaining the BER Lower Bound in a CSK Setting , T. Papamarkou & A. J. Lawrance. Circuits, Systems, & Signal Processing, 32(1):143-166, (2013).
Genetic Determinants of Lipid Homeostasis, E.H. Young, T. Papamarkou, N.W.J. Wainwright & M.S. Sandhu. Best Practice & Research - Clinical Endocrinology & Metabolism, 26(2):203-9, (2012).
Meta Analysis of Candidate Gene Variants Outside the LPA Locus with Lp(a) Plasma Levels in 14,500 participants of Six White European Cohorts. D. Zabaneh, M. Kumari, M. Sandhu, N. Wareham, N. Wainwright, T. Papamarkou et al. Atherosclerosis, 17(2):447-51, (2011).