Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Fig 3
Diagnostic plots of model fits.
The raw data (blue dots) and corresponding fit is visualized in the top row alongside 95, 90, and 68% likelihood bands of the model. Linear and logistic models were fitted to synthetic data to show three kinds of lack-of-fit error (columns 1–3) in comparison to a perfect fit (column 4). The underlying structure of the data and model is as follows: A: Homoscedastic linear model, fitted to homoscedastic nonlinear data. B: Homoscedastic linear model, fitted to heteroscedastic linear data. C: Homoscedastic linear model, fitted to homoscedastic linear data that is Lognormal-distributed. D: Heteroscedastic logistic model, fitted to heteroscedastic logistic data. The residual plots in the middle row show the distance between the data and the modeled location parameter (green line). The bottom row shows how many data points fall into the percentiles of the predicted probability distribution. Whereas the lack-of-fit cases exhibit systematic under- and over-occupancy of percentiles, only in the perfect fit case all percentiles are approximately equally occupied.