Research outputs
Selected research outputs
2025
Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor
Scalable Sequential Monte Carlo Samplers for Numerical Bayesian Inference
Carter, M. (2025, March 13). Scalable Sequential Monte Carlo Samplers for Numerical Bayesian Inference. (University of Liverpool).
2024
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject
Devlin, L., Carter, M., Horridge, P., Green, P. L., & Maskell, S. (2024). The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject. IEEE Signal Processing Letters, 1-5. doi:10.1109/lsp.2024.3386494
2023
Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models
Rosato, C., Moore, R. E., Carter, M., Heap, J., Harris, J., Storopoli, J., & Maskell, S. (2023). Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. Information, 14(3), 170. doi:10.3390/info14030170
2021
Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths
2018
An Empirical Analysis of the Taylor Rule and its Application to Monetary Policy: A Case for the United Kingdom and Euro Area
Bhattarai, K., & Carter, M. (2018). An Empirical Analysis of the Taylor Rule and its Application to Monetary Policy: A Case for the United Kingdom and Euro Area. Asian Journal of Economics and Empirical Research, 5(2), 173-182. doi:10.20448/journal.501.2018.52.173.182