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pro vyhledávání: '"Werner, Maximilian"'
Classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of earthquakes for decades. Recent advances have led to Neural Point Processes (NPPs), whi
Externí odkaz:
http://arxiv.org/abs/2410.08226
Performing Bayesian inference for the Epidemic-Type Aftershock Sequence (ETAS) model of earthquakes typically requires MCMC sampling using the likelihood function or estimating the latent branching structure. These tasks have computational complexity
Externí odkaz:
http://arxiv.org/abs/2404.16590
Autor:
Lapins, Sacha, Butcher, Antony, Kendall, J. -Michael, Hudson, Thomas S., Stork, Anna L., Werner, Maximilian J., Gunning, Jemma, Brisbourne, Alex M.
This article presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e., pre-determined example
Externí odkaz:
http://arxiv.org/abs/2304.08120
Point processes have been dominant in modeling the evolution of seismicity for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most popular. Recent advances in machine learning have constructed highly flexible point process mod
Externí odkaz:
http://arxiv.org/abs/2301.09948
Autor:
Werner, Maximilian Jonas
Publikováno v:
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Thesis (Ph. D.)--UCLA, 2008.
Vita. Includes bibliographical references (leaves 277-308).
Vita. Includes bibliographical references (leaves 277-308).
Externí odkaz:
http://proquest.umi.com/pqdweb?did=1666898521&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD
Operational earthquake forecasting for risk management and communication during seismic sequences depends on our ability to select an optimal forecasting model. To do this, we need to compare the performance of competing models with each other in pro
Externí odkaz:
http://arxiv.org/abs/2105.12065
Akademický článek
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Autor:
Peters, Ole, Werner, Maximilian
Recent studies have shown that many results published in peer-reviewed scientific journals are not reproducible. This raises the following question: why is it so easy to fool myself into believing that a result is reliable when in fact it is not? Usi
Externí odkaz:
http://arxiv.org/abs/1706.07773