Published in Journal of the American Statistical Association, 2023
Recommended citation: Turnbull, K., Lunagómez, S., Nemeth, C. and Airoldi, E., (2019). "Latent space modelling of hypergraph data." Journal of the American Statistical Association (to appear). https://arxiv.org/abs/1909.00472
Published in NeurIPS, 2023
Recommended citation: Sharrock, L., Mackey, L. and Nemeth, C. (2023). "Learning Rate Free Bayesian Inference in Constrained Domains" NeurIPS 2023 (to appear).. https://arxiv.org/abs/2305.14943
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
Published in Bayesian Analysis (to appear), 2023
Recommended citation: Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P. and Fox, E.B., (2023). "Stochastic Gradient MCMC for Nonlinear State Space Models." Bayesian Analysis (to appear). https://arxiv.org/abs/1901.10568
Published in arXiv preprint, 2023
Recommended citation: Sharrock, L., Dodd, D. and Nemeth, C. (2023). "CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models" arXiv preprint.. https://arxiv.org/abs/2305.14916
Published in ICML, 2023
Recommended citation: Sharrock, L. and Nemeth, C. (2023). "Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates." Proceedings of the 40th International Conference on Machine Learning. PMLR 202:30850-30882. https://proceedings.mlr.press/v202/sharrock23a/sharrock23a.pdf
Published in AISTATS, 2023
Recommended citation: Cabezas, A. and Nemeth, C., (2023). "Transport Elliptical Slice Sampling." Proceedings of The 26th International Conference on Artificial Intelligence and Statistics.PMLR 206:3664-3676, 2023. https://proceedings.mlr.press/v206/cabezas23a.html
Published in AISTATS, 2023
Recommended citation: Putcha, S., Nemeth, C. and Fearnhead, P., (2023). "Preferential Subsampling for Stochastic Gradient Langevin Dynamics." Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. PMLR 206:8837-8856, 2023. https://proceedings.mlr.press/v206/putcha23a.html
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. https://academic.oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlad032/7163085
Published in Statistics and Computing, 2023
Recommended citation: Coullon, J., South, L. and Nemeth, C., (2023). "Efficient and generalizable tuning strategies for stochastic gradient MCMC." Statistics and Computing. 33(66). https://link.springer.com/article/10.1007/s11222-023-10233-3
Published in Stat, 2022
Recommended citation: Vyner, C., Nemeth, C. and Sherlock, C., (2022). "SwISS: A Scalable Markov chain Monte Carlo Divide‐and‐Conquer Strategy." Stat, p.e523. https://onlinelibrary.wiley.com/doi/10.1002/sta4.523
Published in Computational Statistics and Data Analysis, 2022
Recommended citation: Turnbull, K., Nemeth, C., Nunes, M. and McCormick, T., (2023). "Sequential estimation of temporally evolving latent space network models." Computational Statistics & Data Analysis, 179, p.107627. https://www.sciencedirect.com/science/article/pii/S0167947322002079
Published in arXiv, 2022
Recommended citation: Bolt, G., Lunagómez, S. and Nemeth, C., (2022). "Modelling Populations of Interaction Networks via Distance Metrics." arXiv. https://arxiv.org/abs/2206.09995
Published in arXiv, 2022
Recommended citation: Bolt, G., Lunagómez, S. and Nemeth, C., (2022). "Distances for comparing multisets and sequences." arXiv. https://arxiv.org/abs/2206.08858
Published in Biometrika, 2022
Recommended citation: South, L.F., Karvonen, T., Nemeth, C., Girolami, M. and Oates, C.J., (2022). "Semi-exact control functionals from Sard’s method." Biometrika, 109(2), pp.351-367. https://academic.oup.com/biomet/article/109/2/351/6309456
Published in Journal of Statistical Software, 2022
Recommended citation: Fairbrother, J., Nemeth, C., Rischard, M., Brea, J. and Pinder, T., (2022). "GaussianProcesses. jl: A nonparametric Bayes package for the Julia language." Journal of Statistical Software, 102, pp.1-36. https://www.jstatsoft.org/article/view/v102i01
Published in Journal of Open Source Software, 2022
Recommended citation: Coullon, J. and Nemeth, C., (2022). "SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms." Journal of Open Source Software, 7(72), p.4113. https://joss.theoj.org/papers/10.21105/joss.04113.pdf
Published in arXiv, 2021
Recommended citation: Pinder, T., Turnbull, K., Nemeth, C. and Leslie, D., (2021). "Gaussian processes on hypergraphs." arXiv. https://arxiv.org/abs/2106.01982
Published in arXiv, 2021
Recommended citation: Cabezas, A., Battiston, M. and Nemeth, C., (2021). "Robust Bayesian Nonparametric Variable Selection for Linear Regression." arXiv. https://arxiv.org/abs/2105.11022
Published in arXiv, 2021
Recommended citation: Pinder, T., Hollaway, M., Nemeth, C., Young, P.J. and Leslie, D., (2021). "A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK." arXiv. https://arxiv.org/abs/2104.10979
Published in Journal of the American Statistical Association, 2021
Recommended citation: Nemeth, C. and Fearnhead, P., (2021). "Stochastic gradient markov chain monte carlo." Journal of the American Statistical Association, 116(533), pp.433-450. https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1847120
Published in arXiv, 2020
Recommended citation: Pinder, T., Nemeth, C. and Leslie, D., (2020). "Stein variational Gaussian processes." arXiv. https://arxiv.org/abs/2009.12141
Published in The Cryosphere, 2020
Recommended citation: Verjans, V., Leeson, A.A., Nemeth, C., Stevens, C.M., Kuipers Munneke, P., Noël, B. and van Wessem, J.M., (2020). "Bayesian calibration of firn densification models." The Cryosphere, 14(9), pp.3017-3032. https://tc.copernicus.org/articles/14/3017/2020/tc-14-3017-2020-discussion.html
Published in NeurIPS, 2019
Recommended citation: Nemeth, C., Lindsten, F., Filippone, M. and Hensman, J., (2019). "Pseudo-extended Markov chain Monte Carlo." Advances in Neural Information Processing Systems, 32. https://proceedings.neurips.cc/paper/2019/hash/e3ca0449fa2ea7701a7ac53fb719c51a-Abstract.html
Published in Journal of Statistical Software, 2019
Recommended citation: Baker, J., Fearnhead, P., Fox, E. B., & Nemeth, C. (2019). "sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo." Journal of Statistical Software, 91(3), 1–27. https://www.jstatsoft.org/article/view/v091i03
Published in Statistics and Computing, 2019
Recommended citation: Baker, J., Fearnhead, P., Fox, E.B. and Nemeth, C., (2019). "Control variates for stochastic gradient MCMC." Statistics and Computing, 29, pp.599-615. https://link.springer.com/article/10.1007/s11222-018-9826-2
Published in NeurIPS, 2018
Recommended citation: Baker, J., Fearnhead, P., Fox, E. and Nemeth, C., (2018). "Large-scale stochastic sampling from the probability simplex." Advances in Neural Information Processing Systems, 31. https://proceedings.neurips.cc/paper/2018/hash/900c563bfd2c48c16701acca83ad858a-Abstract.html
Published in Bayesian Analysis, 2018
Recommended citation: Nemeth, C. and Sherlock, C. "Merging MCMC Subposteriors through Gaussian-Process Approximations." Bayesian Analysis. 13 (2) 507 - 530. https://projecteuclid.org/journals/bayesian-analysis/volume-13/issue-2/Merging-MCMC-Subposteriors-through-Gaussian-Process-Approximations/10.1214/17-BA1063.full
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
Published in Biometrika, 2016
Recommended citation: Nemeth, C., Sherlock, C. and Fearnhead, P., (2016). "Particle metropolis-adjusted Langevin algorithms." Biometrika, 103(3), pp.701-717. https://academic.oup.com/biomet/article-abstract/103/3/701/1743614
Published in PhD Thesis, 2014
Recommended citation: Nemeth, C.J., (2014). "Parameter estimation for state space models using sequential Monte Carlo algorithms." PhD Thesis. Lancaster University (United Kingdom). https://www.proquest.com/openview/b69ef75883fa333b16874bf903d0074e/1?pq-origsite=gscholar&cbl=51922
Published in IEEE Transactions on Signal Processing, 2014
Recommended citation: Nemeth, C., Fearnhead, P. and Mihaylova, L., (2014). "Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments." IEEE Transactions on Signal Processing, 62(5), pp.1245-1255. https://ieeexplore.ieee.org/abstract/document/6692890
Published in 15th International Conference on Information Fusion, 2012
Recommended citation: Nemeth, C., Fearnhead, P., Mihaylova, L. and Vorley, D. (2012). "Bearings-only tracking with particle filtering for joint parameter learning and state estimation," 15th International Conference on Information Fusion, pp. 824-831. https://ieeexplore.ieee.org/abstract/document/6289887
Published in 9th IET Data Fusion and Target Tracking Conference, 2012
Recommended citation: Nemeth, C., Fearnhead, P., Mihaylova, L. and Vorley, D. (2012). "Bearings-only tracking with particle filtering for joint parameter learning and state estimation," 15th International Conference on Information Fusion, pp. 824-831. https://ieeexplore.ieee.org/abstract/document/6253625