| Observation of gravitational waves from a binary black hole merger BP Abbott, R Abbott, TD Abbott, MR Abernathy, F Acernese, K Ackley, ... Physical review letters 116 (6), 061102, 2016 | 17037 | 2016 |
| GW170817: observation of gravitational waves from a binary neutron star inspiral BP Abbott, R Abbott, TD Abbott, F Acernese, K Ackley, C Adams, T Adams, ... Physical review letters 119 (16), 161101, 2017 | 10316 | 2017 |
| Bayesian nonparametric spectral density estimation using B-spline priors MC Edwards, R Meyer, N Christensen Statistics and Computing 29, 67-78, 2019 | 44 | 2019 |
| Identifying and addressing nonstationary LISA noise MC Edwards, P Maturana-Russel, R Meyer, J Gair, N Korsakova, ... Physical Review D 102 (8), 084062, 2020 | 34 | 2020 |
| Bayesian semiparametric power spectral density estimation with applications in gravitational wave data analysis MC Edwards, R Meyer, N Christensen Physical Review D 92 (6), 064011, 2015 | 33 | 2015 |
| Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis C Kirch, MC Edwards, A Meier, R Meyer Bayesian Analysis 14 (4), 1037-1073, 2019 | 32 | 2019 |
| Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations MC Edwards, R Meyer, N Christensen Inverse Problems 30 (11), 114008, 2014 | 30 | 2014 |
| Classifying the equation of state from rotating core collapse gravitational waves with deep learning MC Edwards Physical Review D 103 (2), 024025, 2021 | 25 | 2021 |
| Constraining the spin parameter of near-extremal black holes using LISA O Burke, JR Gair, J Simón, MC Edwards Physical Review D 102 (12), 124054, 2020 | 21 | 2020 |
| Computational techniques for parameter estimation of gravitational wave signals R Meyer, MC Edwards, P Maturana‐Russel, N Christensen Wiley Interdisciplinary Reviews: Computational Statistics 14 (1), e1532, 2022 | 7 | 2022 |
| Calibrating approximate Bayesian credible intervals of gravitational-wave parameters R Mao, JE Lee, O Burke, AJK Chua, MC Edwards, R Meyer Physical Review D 109 (8), 083002, 2024 | 1 | 2024 |
| Bayesian modelling of stellar core collapse gravitational wave signals and detector noise M Edwards PhD Thesis-University of Auckland, 2017 | 1 | 2017 |
| Generative adversarial network for stellar core-collapse gravitational waves T Eccleston, MC Edwards Physical Review D 110, 104055, 2024 | | 2024 |
| A novel stacked hybrid autoencoder for imputing LISA data gaps R Mao, JE Lee, MC Edwards arXiv preprint arXiv:2410.05571, 2024 | | 2024 |
| Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification YS Abylkairov, MC Edwards, D Orel, A Mitra, B Shukirgaliyev, ... arXiv preprint arXiv:2409.14508, 2024 | | 2024 |
| Package ‘bsplinePsd’ MC Edwards, R Meyer, N Christensen, MMC Edwards, I Rcpp | | 2018 |
| Signal extraction and power spectral density estimation: A Bayesian semi-parametric approach M Edwards, R Meyer, NL Christensen 30th International Workshop on Statistical Modelling, 2015 | | 2015 |
Renate MeyerProfessor of Statistics, University of AucklandVerified email at stat.auckland.ac.nz
