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Christopher Nemeth
Christopher Nemeth
Professor of Statistics, Lancaster University, UKRI Turing AI Fellow
Verified email at lancaster.ac.uk - Homepage
Title
Cited by
Cited by
Year
Stochastic Gradient Markov Chain Monte Carlo
C Nemeth, P Fearnhead
Journal of the American Statistical Association 116 (533), 433-450, 2021
1252021
Control variates for stochastic gradient MCMC
J Baker, P Fearnhead, EB Fox, C Nemeth
Statistics and Computing 29, 599-615, 2019
1142019
Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments
C Nemeth, P Fearnhead, L Mihaylova
IEEE Transactions on Signal Processing 62 (5), 1245-1255, 2014
992014
Merging MCMC Subposteriors through Gaussian-Process Approximations
C Nemeth, C Sherlock
Bayesian Analysis 13 (2), 507-530, 2018
542018
Particle approximations of the score and observed information matrix for parameter estimation in state–space models with linear computational cost
C Nemeth, P Fearnhead, L Mihaylova
Journal of Computational and Graphical Statistics 25 (4), 1138-1157, 2016
512016
Particle Metropolis-adjusted Langevin algorithms
C Nemeth, C Sherlock, P Fearnhead
Biometrika 103 (3), 701-717, 2016
382016
Gaussianprocesses. jl: A nonparametric bayes package for the julia language
J Fairbrother, C Nemeth, M Rischard, J Brea, T Pinder
Journal of Statistical Software 102, 1-36, 2022
32*2022
Bayesian calibration of firn densification models
V Verjans, AA Leeson, C Nemeth, CM Stevens, P Kuipers Munneke, ...
The Cryosphere Discussions 2020, 1-23, 2020
212020
Semi-exact control functionals from Sard’s method
LF South, T Karvonen, C Nemeth, M Girolami, CJ Oates
Biometrika 109 (2), 351-367, 2022
202022
sgmcmc: An R package for stochastic gradient Markov chain Monte Carlo
J Baker, P Fearnhead, EB Fox, C Nemeth
Journal of Statistical Software 91 (1), 1-27, 2019
152019
Pseudo-extended Markov chain Monte Carlo
C Nemeth, F Lindsten, M Filippone, J Hensman
Advances in Neural Information Processing Systems 32, 2019
152019
Particle learning methods for state and parameter estimation
C Nemeth, P Fearnhead, L Mihaylova, D Vorley
9th IET Data Fusion and Target Tracking Conference, London, U.K., 2012
142012
Latent Space Modeling of Hypergraph Data
K Turnbull, S Lunagómez, C Nemeth, E Airoldi
Journal of the American Statistical Association, 1-13, 2023
13*2023
Stochastic gradient MCMC for nonlinear state space models
C Aicher, S Putcha, C Nemeth, P Fearnhead, E Fox
Bayesian Analysis 1 (1), 1-23, 2023
13*2023
Gaussian processes on hypergraphs
T Pinder, K Turnbull, C Nemeth, D Leslie
arXiv preprint arXiv:2106.01982, 2021
82021
Stein variational Gaussian processes
T Pinder, C Nemeth, D Leslie
arXiv preprint arXiv:2009.12141, 2020
72020
SwISS: A scalable Markov chain Monte Carlo divide‐and‐conquer strategy
C Vyner, C Nemeth, C Sherlock
Stat 12 (1), e523, 2023
62023
Large-Scale Stochastic Sampling from the Probability Simplex
J Baker, P Fearnhead, EB Fox, C Nemeth
Advances in Neural Information Processing Systems 31, 6720-6730, 2018
62018
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
L Sharrock, C Nemeth
International Conference on Machine Learning 202, 30850-30882, 2023
52023
Efficient and generalizable tuning strategies for stochastic gradient MCMC
J Coullon, L South, C Nemeth
Statistics and Computing 33 (3), 66, 2023
52023
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