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Research

Professor Christopher Nemeth

Professor in Statistics, Research Student

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  1. Published

    Particle Metropolis-adjusted Langevin algorithms

    Nemeth, C., Sherlock, C. & Fearnhead, P., 09/2016, In: Biometrika. 103, 3, p. 701-717 17 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  2. Published

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

    Nemeth, C., Fearnhead, P. & Mihaylova, L. S., 11/2016, In: Journal of Computational and Graphical Statistics. 25, 4, p. 1138-1157 20 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  3. Published

    Merging MCMC subposteriors through Gaussian-Process Approximations

    Nemeth, C. & Sherlock, C. G., 03/2018, In: Bayesian Analysis. 13, 2, p. 507-530 24 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  4. Published

    Pseudo-extended Markov chain Monte Carlo

    Nemeth, C., Lindsten, F., Filippone, M. & Hensman, J., 8/12/2019, p. 1-11. 11 p.

    Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

  5. Published

    Stochastic gradient Markov chain Monte Carlo

    Nemeth, C. & Fearnhead, P., 30/03/2021, In: Journal of the American Statistical Association. 1116, 533, p. 433-450 18 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  6. E-pub ahead of print

    Seconder of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’

    Nemeth, C., 2/01/2024, (E-pub ahead of print) In: Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  7. Published

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

    Oyebamiji, O., Nemeth, C., Harrison, P., Dunford, R. & Cojocaru, G., 24/01/2022, Arxiv.

    Research output: Working paper › Preprint

  8. Published

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

    Oyebamiji, O., Nemeth, C. J., Harrison, P., Dunford, R. & Cojocaru, G., 13/06/2023, In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 72, 3, p. 770-808 39 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  9. Forthcoming

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

    Papamarkou, T., Skoularidou, M., Palla, K., Aitchison, L., Arbel, J., Dunson, D., Filippone, M., Fortuin, V., Hennig, P., Hernández-Lobato, J. M., Hubin, A., Immer, A., Karaletsos, T., Khan, M. E., Kristiadi, A., Li, Y., Mandt, S., Nemeth, C., Osborne, M. A., Rudner, T. G. J., & 5 othersRügamer, D., Teh, Y. W., Welling, M., Wilson, A. G. & Zhang, R., 1/05/2024, (Accepted/In press) In: Proceedings of Machine Learning Research.

    Research output: Contribution to Journal/Magazine › Conference article › peer-review

  10. Published

    Stein Variational Gaussian Processes

    Pinder, T., Nemeth, C. & Leslie, D., 25/09/2020, In: arXiv.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

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