@article{vanderAaLeopoldWeidlich2020,
  author    = {van der Aa, Han and Leopold, Henrik and Weidlich, Matthias},
  title     = {Partial order resolution of event logs for process conformance checking},
  series = {Decision support systems : DSS},
  volume    = {136},
  journal   = {Decision support systems : DSS},
  publisher = {Elsevier},
  address   = {Amsterdam [u.a.]},
  issn      = {0167-9236},
  doi       = {10.1016/j.dss.2020.113347},
  pages     = {12},
  year      = {2020},
  abstract  = {While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.},
  language  = {en}
}
@article{WeatherillCotton2020,
  author    = {Weatherill, Graeme and Cotton, Fabrice Pierre},
  title     = {A ground motion logic tree for seismic hazard analysis in the stable cratonic region of Europe},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {18},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  number    = {14},
  publisher = {Springer Science + Business Media B.V.},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-020-00940-x},
  pages     = {6119 -- 6148},
  year      = {2020},
  abstract  = {Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification.},
  language  = {en}
}
@article{Korup2020,
  author    = {Korup, Oliver},
  title     = {Bayesian geomorphology},
  series = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group},
  volume    = {46},
  journal   = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0197-9337},
  doi       = {10.1002/esp.4995},
  pages     = {151 -- 172},
  year      = {2020},
  abstract  = {The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.},
  language  = {en}
}