Uncertainty quantification

December 26, 2016 — June 6, 2024

stochastic processes

Using machine learning to make predictions, with a measure of the confidence of those predictions.

Closely related, perhaps identical: sensitivity analysis.

Figure 1

1 Taxonomy

Should clarify. TBD. Here is a recent reference on the theme: Kendall and Gal (2017) This extricates aleatoric and epistemic uncertainty. Also to mention, model uncertainty.

2 DUQ networks

van Amersfoort et al. (2020);Kendall and Gal (2017)

3 Bayes

Bayes methods have some ideas of uncertainty baked in. You can get some way with e.g., e.g. Gaussian process regression, or probabilistic NNs.

4 Physical model calibration

PEST, PEST++, and pyemu are some integrated systems for uncertainty quantification that use some weird terminology, such a FOSM (First-order-second-moment) models. I think these might be best considered as inverse problem solvers, and the uncertainty quantification is a side effect of the inversion.

5 Conformal prediction

See conformal prediction.

6 Uncertainty Quantification 360

Figure 2: Source: UQ360

IBM’s Uncertainty Quantification 360 toolkit a summary of popular generic methods:

  • Auxiliary Interval Predictor

Use an auxiliary model to improve the calibration of UQ generated by the original model.

  • Blackbox Metamodel Classification

Extract confidence scores from trained black-box classification models using a meta-model.

  • Blackbox Metamodel Regression

Extract prediction intervals from trained black-box regression models using a meta-model.

  • Classification Calibration

Post-hoc calibration of classification models using Isotonic Regression and Platt Scaling.

  • Heteroscedastic Regression

Train regression models that capture data uncertainty, assuming the targets are noisy and the amount of noise varies between data points.

  • Homoscedastic Gaussian Process Regression

Train Gaussian Process Regression models with homoscedastic noise that capture data and model uncertainty.

  • Horseshoe BNN classification

Train Bayesian neural networks classifiers with Gaussian and Horseshoe priors that capture data and model uncertainty.

  • Horseshoe BNN regression

Train BNNs regression models with Gaussian and Horseshoe priors that capture data and model uncertainty.

  • Infinitesimal Jackknife

Extract uncertainty from trained models by approximating the effect of training data perturbations on the model’s predictions.

  • Quantile Regression

Train Quantile Regression models that capture data uncertainty, by learning two separate models for the upper and lower quantile to obtain the prediction intervals.

  • UCC Recalibration

Recalibrate UQ of a regression model to specified operating point using Uncertainty Characteristics Curve

They provide guidance on method selection in the manual:

7 References

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