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Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?

Sahlin, Ullrika LU (2015) In Journal of Computer-Aided Molecular Design 29(7). p.583-594
Abstract
A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing... (More)
A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing error in predictions and is based on probability modelling of errors where uncertainty is measured by Bayesian probabilities. Even though well motivated, the choice to use Bayesian probabilities is a challenge to statistics and chemical modelling. Fully Bayesian modelling, Bayesian meta-modelling and bootstrapping are discussed as possible approaches. Deciding how to assess uncertainty is an active choice, and should not be constrained by traditions or lack of validated and reliable ways of doing it. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Computer-Aided Molecular Design
volume
29
issue
7
pages
583 - 594
publisher
Kluwer
external identifiers
  • pmid:25491202
  • wos:000357465200001
  • scopus:84943450292
ISSN
1573-4951
DOI
10.1007/s10822-014-9822-3
language
English
LU publication?
yes
id
0563ca8d-5cd5-4f37-80f4-bf1c5ca56214 (old id 4908716)
date added to LUP
2015-02-05 13:28:30
date last changed
2017-01-01 03:52:40
@article{0563ca8d-5cd5-4f37-80f4-bf1c5ca56214,
  abstract     = {A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing error in predictions and is based on probability modelling of errors where uncertainty is measured by Bayesian probabilities. Even though well motivated, the choice to use Bayesian probabilities is a challenge to statistics and chemical modelling. Fully Bayesian modelling, Bayesian meta-modelling and bootstrapping are discussed as possible approaches. Deciding how to assess uncertainty is an active choice, and should not be constrained by traditions or lack of validated and reliable ways of doing it.},
  author       = {Sahlin, Ullrika},
  issn         = {1573-4951},
  language     = {eng},
  number       = {7},
  pages        = {583--594},
  publisher    = {Kluwer},
  series       = {Journal of Computer-Aided Molecular Design},
  title        = {Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?},
  url          = {http://dx.doi.org/10.1007/s10822-014-9822-3},
  volume       = {29},
  year         = {2015},
}