Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Fig 7
Data structures and computation graph of murefi models.
Elements in a comprehensive parameter vector are mapped to replicate-wise model instances. In the depicted example, the model instances for both replicates “B1” and “B2” share θ1,global as the first element in their parameter vectors. The second model parameter θ2 is local to the replicates, hence the full parameter vector (left) is comprised of three elements. Model predictions are made such that they resemble the structure of the observed data, having the same number of time points for each predicted time series. An objective function calculating the sum of log-likelihoods is created by associating predicted and observed time series via their respective calibration models. By associating the calibration models based on the dependent variable name, a calibration model may be re-used across multiple replicates, or kept local if, for example, the observations were obtained by different methods.