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@book{Bar-Shalom:1987,
 author = {Bar-Shalom, Y.},
 title = {Tracking and Data Association},
 year = {1987},
 isbn = {0-120-79760-7},
 publisher = {Academic Press Professional, Inc.},
 address = {San Diego, CA, USA},
}
@ARTICLE{Cox93areview,
    author = {Ingemar J. Cox},
    title = {A Review of Statistical Data Association Techniques for Motion Correspondence},
    journal = {International Journal of Computer Vision},
    year = {1993},
    volume = {10},
    pages = {53--66}
}
@inproceedings{hans_efficient_2009,
  author = {Hans, Alexander and Udluft, Steffen},
  booktitle = {International {{Conference}} on {{Artificial Neural Networks}}},
  date = {2009},
  pages = {70--79},
  publisher = {{Springer}},
  title = {Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data}
}
@article{tresp_wet_1994,
  author = {Tresp, Volker},
  date = {1994},
  journaltitle = {Siemens AG, CT IC 4, Technical Report},
  title = {The Wet Game of Chicken}
}
@article{kaiser_interpretable_2019,
  abstract = {In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.},
  author = {Kaiser, Markus and Otte, Clemens and Runkler, Thomas and Ek, Carl Henrik},
  date = {2019},
  journaltitle = {Computational Intelligence and Machine Learning},
  langid = {english},
  pages = {6},
  title = {Interpretable {{Dynamics Models}} for {{Data}}-{{Efficient Reinforcement Learning}}},
  volume = {ESANN 2019 proceedings}
}