Current work

Markov melding methodology and applications. I wrote much of my PhD thesis on melding, a near final version of my thesis this available here.

Publications

Manderson, A.A, Goudie, R.J.B. (2022). Combining Chains of Bayesian Models with Markov Melding. Bayesian Analysis, Advance Publication. DOI: 10.1214/22-BA1327

Manderson, A.A, Goudie, R.J.B. (2022). A numerically stable algorithm for integrating Bayesian models using Markov melding. Statistics and Computing 32, 24. DOI: 10.1007/s11222-022-10086-2

Manderson, A., Rayson, M., Cripps, E., Girolami, M., Gosling, J.P., Hodkiewicz, M., Jones, N. and Ivey, G. (2019). Uncertainty quantification of density and stratification estimates with implications for predicting ocean dynamics. Journal of Atmospheric and Oceanic Technology, 36: 1313–1330.
DOI: 10.1175/JTECH-D-18-0200.1

Crispe, E. J., Secombe, C. J., Perera, D. I., Manderson, A. A., Turlach, B. A. and Lester, G. D. (2019), Exercise‐induced pulmonary haemorrhage in Thoroughbred racehorses: a longitudinal study. Equine Veterinary Journal, 51: 45-51.
DOI: 10.1111/evj.12957

Manderson, A.A., Murray, K. and Turlach, B.A. (2018). Dynamic Bayesian forecasting of AFL match results using the Skellam distribution, Australian & New Zealand Journal of Statistics 60(2): 174-187.
DOI: 10.1111/anzs.12225

Manderson, A.A., Cripps, E., Murray, K. and Turlach, B.A. (2017). Monotone polynomials using BUGS and Stan, Australian & New Zealand Journal of Statistics 59(4): 353–370.
DOI: 10.1111/anzs.12207

Book chapters

Manderson, A.A., Murray, K., Turlach, B.A., (2019). Flexible Regression Modelling Under Shape Constraints, in: Fan, Y., Nott, D., Smith, M.S., Dortet-Bernadet, J.-L. (Eds.), Flexible Bayesian Regression Modelling. Academic Press, pp. 251–279.

MPhil Thesis

Methodology for Bayesian monotonic polynomials (2018), The University of Western Australia. Supervised by: Turlach, B.A. and Murray, K.
DOI: 10.4225/23/5b33343ad69b4

Links

My ORCID and Google scholar.