@article{MalemShinitskiOjedaOpper2022,
  author    = {Malem-Shinitski, Noa and Ojeda, Cesar and Opper, Manfred},
  title     = {Variational bayesian inference for nonlinear hawkes process with gaussian process self-effects},
  series = {Entropy},
  volume    = {24},
  journal   = {Entropy},
  number    = {3},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1099-4300},
  doi       = {10.3390/e24030356},
  pages     = {22},
  year      = {2022},
  abstract  = {Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process. Whereas previous work either relies on a less flexible parameterization of the model, or requires a large amount of data, our formulation allows for both a flexible model and learning when data are scarce. We continue the line of work of Bayesian inference for Hawkes processes, and derive an inference algorithm by performing inference on an aggregated sum of Gaussian Processes. Approximate Bayesian inference is achieved via data augmentation, and we describe a mean-field variational inference approach to learn the model parameters. To demonstrate the flexibility of the model we apply our methodology on data from different domains and compare it to previously reported results.},
  language  = {en}
}
@article{HempelAdolphsLandwehretal.2020,
  author    = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Janke, David and Amon, Thomas},
  title     = {How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn—classical statistics versus machine learning},
  series = {Sustainability},
  volume    = {12},
  journal   = {Sustainability},
  number    = {3},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2071-1050},
  doi       = {10.3390/su12031030},
  pages     = {18},
  year      = {2020},
  abstract  = {Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20\%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.},
  language  = {en}
}