Data Science of the Natural Environment
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projects
publications
Particle learning methods for state and parameter estimation
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
Bearings-only tracking with particle filtering for joint parameter learning and state estimation
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
Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments
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
Parameter estimation for state space models using sequential Monte Carlo algorithms
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
Particle Metropolis-adjusted Langevin algorithms
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
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
Merging MCMC Subposteriors through Gaussian-Process Approximations
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
Large-Scale Stochastic Sampling from the Probability Simplex
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
Stochastic Gradient MCMC for Nonlinear State Space Models
Published in arXiv, 2019
Recommended citation: Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P. and Fox, E.B., (2019). "Stochastic Gradient MCMC for Nonlinear State Space Models." arXiv. https://arxiv.org/abs/1901.10568
Control variates for stochastic gradient MCMC
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
Latent space modelling of hypergraph data
Published in arXiv, 2019
Recommended citation: Turnbull, K., Lunagómez, S., Nemeth, C. and Airoldi, E., (2019). "Latent space modelling of hypergraph data." arXiv. https://arxiv.org/abs/1909.00472
sgmcmc: An R package for stochastic gradient Markov chain Monte Carlo
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
Pseudo-extended Markov chain Monte Carlo
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
Bayesian calibration of firn densification models
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
Stein variational Gaussian processes
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
Stochastic Gradient Markov Chain Monte Carlo
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
A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK
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
Robust Bayesian Nonparametric Variable Selection for Linear Regression
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
Gaussian processes on hypergraphs
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
SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms
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
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
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
Semi-exact control functionals from Sard’s method
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
Distances for Comparing Multisets and Sequences
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
Modelling Populations of Interaction Networks via Distance Metrics
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
Sequential Estimation of Temporally Evolving Latent Space Network Models
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
SwISS: A scalable Markov chain Monte Carlo divide-and-conquer strategy
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
Efficient and generalizable tuning strategies for stochastic gradient MCMC
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
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. https://academic.oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlad032/7163085
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
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
Transport Elliptical Slice Sampling
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
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Published in ICML, 2023
Recommended citation: Sharrock, L. and Nemeth, C. (2023). "Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates." ICML (to appear). https://arxiv.org/abs/2301.11294
CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models
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
Learning Rate Free Bayesian Inference in Constrained Domains
Published in arXiv preprint, 2023
Recommended citation: Sharrock, L., Mackey, L. and Nemeth, C. (2023). "Learning Rate Free Bayesian Inference in Constrained Domains" arXiv preprint.. https://arxiv.org/abs/2305.14943
software
GaussianProcesses.jl
A Julia package for Gaussian processes.
Stochastic gradient MCMC in R
An R package for stochastic gradient Monte Carlo sampling based on Tensorflow.
SGMCMCJax
A Python package based on JAX for stochastic gradient Monte Carlo sampling.