(337d) Uncertainty Propagation for Probabilistic Prediction in Partial Least Squares Using Bootstrap Methods
AIChE Annual Meeting
Tuesday, November 15, 2022 - 1:33pm to 1:54pm
The main result of this work is a novel probabilistic prediction from PLS, which we demonstrate can differ significantly from predictions that avoid dealing with the non-linear dependence of the predictions on the training data's observed outputs. The benefit of these probabilistic predictions are demonstrated in design space identification. First, we use simulated data to compare our method's performance to those from a perfect model. In addition, we investigate our method's performance on real world data and demonstrate its ability to make predictions that are a reasonable representation of the difference between the model prediction and the true observed data.
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