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Modelling and Bayesian analysis of the Abakaliki smallpox data (2016)
Journal Article
Stockdale, J. E., Kypraios, T., & O’Neill, P. D. (2017). Modelling and Bayesian analysis of the Abakaliki smallpox data. Epidemics, 19, https://doi.org/10.1016/j.epidem.2016.11.005

The celebrated Abakaliki smallpox data have appeared numerous times in the epidemic modelling literature, but in almost all cases only a specific subset of the data is considered. The only previous analysis of the full data set relied on approximatio... Read More about Modelling and Bayesian analysis of the Abakaliki smallpox data.

A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds (2016)
Journal Article
Down, P., Bradley, A., Breen, J., Browne, W., Kypraios, T., & Green, M. (2016). A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds. Preventive Veterinary Medicine, 133, 64-72. https://doi.org/10.1016/j.prevetmed.2016.09.012

Importance of the dry period with respect to mastitis control is now well established although the precise interventions that reduce the risk of acquiring intramammary infections during this time are not clearly understood. There are very few interve... Read More about A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds.

A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation (2016)
Journal Article
Kypraios, T., Neal, P., & Prangle, D. (2017). A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation. Mathematical Biosciences, 287, https://doi.org/10.1016/j.mbs.2016.07.001

Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for t... Read More about A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation.

Bayesian inference and model choice for Holling’s disc equation: a case study on an insect predator-prey system (2016)
Journal Article
Papanikolaou, N., Williams, H., Demiris, N., Preston, S., Milonas, P., & Kypraios, T. (2016). Bayesian inference and model choice for Holling’s disc equation: a case study on an insect predator-prey system. Community Ecology, 17(1), 71-78. https://doi.org/10.1556/168.2016.17.1.9

The dynamics of predator-prey systems relate strongly to the density (in)dependent attributes of the predator’s feeding rate, i.e., its functional response. The outcome of functional response models is often used in theoretical or applied ecology in... Read More about Bayesian inference and model choice for Holling’s disc equation: a case study on an insect predator-prey system.

Statistically efficient tomography of low rank states with incomplete measurements (2016)
Journal Article
Acharya, A., Kypraios, T., & Gut?a?, M. (2016). Statistically efficient tomography of low rank states with incomplete measurements. New Journal of Physics, 18(4), https://doi.org/10.1088/1367-2630/18/4/043018

The construction of physically relevant low dimensional state models, and the design of appropriate measurements are key issues in tackling quantum state tomography for large dimensional systems. We consider the statistical problem of estimating low... Read More about Statistically efficient tomography of low rank states with incomplete measurements.

Reconstructing transmission trees for communicable diseases using densely sampled genetic data (2016)
Journal Article
Worby, C. J., O'Neill, P. D., Kypraios, T., Robotham, J. V., De Angelis, D., Cartwright, E. J., …Cooper, B. S. (2016). Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics, 10(1), https://doi.org/10.1214/15-AOAS898

Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measur... Read More about Reconstructing transmission trees for communicable diseases using densely sampled genetic data.

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes (2016)
Journal Article
Xu, X., Kypraios, T., & O'Neill, P. D. (2016). Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes. Biostatistics, 17(4), 619-633. https://doi.org/10.1093/biostatistics/kxw011

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarel... Read More about Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters (2016)
Journal Article
Gerstgrasser, M., Nicholls, S., Stout, M., Smart, K., Powell, C., Kypraios, T., & Stekel, D. J. (2016). A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. Journal of Bioinformatics and Computational Biology, 14(03), 1-23. https://doi.org/10.1142/S0219720016500074

Biolog phenotype microarrays enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The softwa... Read More about A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters.

Exact Bayesian inference for the Bingham distribution (2016)
Journal Article
Fallaize, C. J., & Kypraios, T. (in press). Exact Bayesian inference for the Bingham distribution. Statistics and Computing, 26(1), https://doi.org/10.1007/s11222-014-9508-7

This paper is concerned with making Bayesian inference from data that are assumed to be drawn from a Bingham distribution. A barrier to the Bayesian approach is the parameter-dependent normalising constant of the Bingham distribution, which, even whe... Read More about Exact Bayesian inference for the Bingham distribution.