Posts by Collection

projects

publications

Particle approximations of the score and observed information matrix for parameter estimation in state–space models with linear computational cost

Published in Journal of Computational and Graphical Statistics, 2016

Recommended citation: Nemeth, C., Fearnhead, P. and Mihaylova, L., (2016). "Particle approximations of the score and observed information matrix for parameter estimation in state–space models with linear computational cost." Journal of Computational and Graphical Statistics, 25(4), pp.1138-1157. https://www.tandfonline.com/doi/abs/10.1080/10618600.2015.1093492

Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

Published in Journal of the Royal Statistical Society: Series C, 2023

Recommended citation: Oyebamiji, O., Nemeth, C., Harrison, P., Dunford, R. and Cojocaru, G., (2023). "Multivariate sensitivity analysis for a large-scale climate impact and adaptation model." Journal of the Royal Statistical Society: Series C. Vol.72(3), pp. 770–808. https://academic.oup.com/jrsssc/article-abstract/72/3/770/7163085?redirectedFrom=fulltext

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

Published in Journal of Glaciology (to appear), 2023

Recommended citation: Shu, Q., Killick, R., Leeson, A., Nemeth, C., Fettweis, X., Hogg, A., & Leslie, D. (2023). "Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data." Journal of Glaciology (to appear). https://www.cambridge.org/core/journals/journal-of-glaciology/article/characterising-the-ice-sheet-surface-in-northeast-greenland-using-sentinel1-sar-data/6FB58B78E79370A93AFCCF53ED2945F5

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI

Published in arXiv preprint, 2024

Recommended citation: Papamarkou, T., Skoularidou, M., Palla, K., Aitchison, L., Arbel, J., Dunson, D., Filippone, M., Fortuin, V., Hennin, P., Hubin, A., Immer, A., Karaletsos, T.,Khan, M. E., Kristiadi, A., Li, Y., Hernandez-Lobato, J., M., Mandt, S., Nemeth, C., Osborne, M. A., Rudner, T.G.J., Rugmer, D,. Teh, Y.W., Welling, M., Wilson, A.G. and Zhang, R. and (2024). "Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI" arXiv preprint.. https://arxiv.org/abs/2402.00809

software

SGMCMCJax

A Python package based on JAX for stochastic gradient Monte Carlo sampling.