Flexible Bayesian Nonlinear Model Configuration

@article{Hubin2021FlexibleBN,
  title={Flexible Bayesian Nonlinear Model Configuration},
  author={Aliaksandr Hubin and Geir Storvik and Florian Frommlet},
  journal={J. Artif. Intell. Res.},
  year={2021},
  volume={72},
  pages={901-942}
}
Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through  flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can… 
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