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A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant (2022)
Journal Article
Hannaford, N., Heaps, S., Nye, T., Curtis, T., Allen, B., Golightly, A., & Wilkinson, D. (2023). A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant. Computational Statistics & Data Analysis, 179, https://doi.org/10.1016/j.csda.2022.107659

Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of the complex network of interactions therein is essential to maximisin... Read More about A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant.

Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices (2022)
Journal Article
Sherlock, C., & Golightly, A. (2023). Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices. Journal of Computational and Graphical Statistics, 32(1), 36-48. https://doi.org/10.1080/10618600.2022.2093886

We present new methodologies for Bayesian inference on the rate parameters of a discretely observed continuous-time Markov jump process with a countably infinite statespace. The usual method of choice for inference, particle Markov chain Monte Carlo... Read More about Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices.

Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK (2022)
Journal Article
Wadkin, L. E., Branson, J., Hoppit, A., Parker, N. G., Golightly, A., & Baggaley, A. W. (2022). Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK. Ecology and Evolution, 12(5), Article e8871. https://doi.org/10.1002/ece3.8871

1. Invasive pests pose a great threat to forest, woodland, and urban tree ecosystems. The oak processionary moth (OPM) is a destructive pest of oak trees, first reported in the UK in 2006. Despite great efforts to contain the outbreak within the orig... Read More about Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK.

Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes (2022)
Journal Article
Golightly, A., & Sherlock, C. (2022). Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes. Statistics and Computing, 32, Article 21. https://doi.org/10.1007/s11222-022-10083-5

We consider the problem of inference for nonlinear, multivariate diffusion processes, satisfying Itô stochastic differential equations (SDEs), using data at discrete times that may be incomplete and subject to measurement error. Our starting point is... Read More about Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes.