| Probabilistic identification of earthquake clusters using rescaled nearest neighbour distance networks K Bayliss, M Naylor, IG Main Geophysical Journal International 217 (1), 487-503, 2019 | 28 | 2019 |
| pyCSEP: a Python toolkit for earthquake forecast developers WH Savran, JA Bayona, P Iturrieta, KM Asim, H Bao, K Bayliss, ... Seismological Society of America 93 (5), 2858-2870, 2022 | 19 | 2022 |
| Data‐driven optimization of seismicity models using diverse data sets: Generation, evaluation, and ranking using Inlabru K Bayliss, M Naylor, J Illian, IG Main Journal of Geophysical Research: Solid Earth 125 (11), e2020JB020226, 2020 | 15 | 2020 |
| Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru K Bayliss, M Naylor, F Kamranzad, I Main Natural Hazards and Earth System Sciences 22 (10), 3231-3246, 2022 | 8 | 2022 |
| Spatio-temporal clustering of earthquakes in the Italian Central Apennines sequence K Bayliss, M Naylor, IG Main AGU Fall Meeting Abstracts 2019, S21E-0561, 2019 | 2 | 2019 |
| RISE deliverable 6.1: Integration of RISE Innovations in the Fields of OELF, RLA and SHM C Nievas, C Crowley, Y Reuland, G Weatherill, G Baltzopoulos, K Bayliss, ... | 1 | 2023 |
| Extending pyCSEP: A Python Toolkit for Earthquake Forecast Developers PJ Maechling, F Silva, PC Iturrieta, K Mensah Graham, H Bao, ... AGU 2023 Fall Meeting, 2023 | | 2023 |
| Exploration of state-dependent rapid loss assessment and event-based operational earthquake loss forecasting incorporating structural health monitoring: an open-source tool C Nievas, H Crowley, Y Reuland, G Weatherill, G Baltzopoulos, K Bayliss, ... SECED 2023 Conference: Earthquake Engineering & Dynamics for a Sustainable …, 2023 | | 2023 |
| Integration of RISE innovations in the fields of OELF, RLA and SHM: input and output datasets (Version 1.0) C Nievas, H Crowley, Y Reuland, G Weatherill, K Bayliss, E Chatzi, ... Zenodo, 2023 | | 2023 |
| Reproducibility Package for pyCSEP: A Toolkit for Earthquake Forecast Developers W Savran, JA Bayona, P Iturrieta, K Asim, H Bao, K Bayliss, M Herrmann, ... | | 2022 |
| Modelling Seismicity in California as a Spatio-Temporal Point Process Using inlabru: Insights for Earthquake Forecasting M Naylor, K Bayliss, F Lindgren, F Serafini, I Main EGU General Assembly Conference Abstracts, 8814, 2020 | | 2020 |
| Spatio-temporal modelling of earthquakes and earthquake clustering KL Bayliss The University of Edinburgh, 2019 | | 2019 |
| Investigating earthquake clustering using probabilistic networks K Bayliss, M Naylor, I Main EGU General Assembly Conference Abstracts, 15479, 2018 | | 2018 |
| Probabilistic assignment of mainshock-aftershock and swarm type clustering K Bayliss, M Naylor, IG Main AGU Fall Meeting Abstracts 2017, NH21A-0160, 2017 | | 2017 |
| Enhancing the ETAS model: incorporating rate-dependent incompleteness, constructing a representative dataset, and reducing bias in inversions F Kamranzad, M Naylor, F Lindgren, K Bayliss, I Main | | |
Mark NaylorSchool of GeoSciences, University of EdinburghBestätigte E-Mail-Adresse bei ed.ac.uk
