Progress has been made in the field of scientifically sound and efficient calibration of parameters of models under use of insider information.
A key point is the visualization of the remaining uncertainty. If many data are available for calibration, the method can lead to unlikely accurate results (over-conditioning).
The aim of this project is to develop a framework of Bayesian Monte Carlo computational methods for monitoring based on (partly) stochastic process models and its associated data series. Another goal is to study how the desired frame can be applied in Wageningen process models.