The Kitchin Research Group

When we fit models to data there are two kinds of uncertainty: the kind that represents uncertainty in the data, e.g. random noise that we cannot fit, and uncertainty in the model, e.g. are we using the right one. With a physics based model, we get model-specific estimates of uncertainty. We show in this paper how to think about and quantify these kinds of errors, and particularly how to use Bayesian models like a Gaussian process to get a model-general error when making predictions about physical properties.

@article{zhan-2022-model-specif,
  author =       {Ni Zhan and John R. Kitchin},
  title =        {Model-Specific To Model-General Uncertainty for Physical
                  Properties},
  journal =      {Industrial \& Engineering Chemistry Research},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.iecr.1c04706},
  year =         2022,
  doi =          {10.1021/acs.iecr.1c04706},
  url =          {http://dx.doi.org/10.1021/acs.iecr.1c04706},
  DATE_ADDED =   {Sun Feb 13 12:08:27 2022},
}

Copyright (C) 2022 by John Kitchin. See the License for information about copying.

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