About us

The areas of research of the Statistical Inference research group include the development of new methodology and software motivated by applications in ecology, epidemiology, biostatistics, finance and genetics. The group works closely with their colleagues within the Statistical Ecology and Statistical Medicine and Molecular Biology research groups.

Research areas

Bayesian inference:
elicitation of prior knowledge, model selection and model-averaging, efficient and generic reversible jump Markov Chain Monte Carlo algorithms, application of sequential Monte Carlo methods to estimation in state-space models
(contact Dr Michail Papathomas for more information)

Methods for integrated modelling of diverse data sources (e.g. genome mapping, genetic, microarray, and proteomics data), methods and software to enable biological inference.
(contact Prof Andy Lynch for more information)

Design of experiments:
block designs, randomization, multi-tiered experiments
(contact Prof. Rosemary Bailey for more information)

Estimation of population size:
capture-recapture methods, distance sampling
(contact Prof. David Borchers for more information)

Model fitting techniques:
ADMB, INLA, sequential Monte Carlo methods (such as particle filters)
(contact Prof Len Thomas for more information)

Smoothing methods:
spatially adaptive smoothing in one and two dimensions, P-splines
(contact Prof Monique MacKenzie for more information)

Statistical genetics:
association mapping methods, fine-scale mapping, heterogeneous populations, inbred populations, genome-wide data
(contact Dr Michail Papathomas for more information)