Uncertainty Analysis of Dynamic Models

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Statistical Uncertainty Analysis of Dynamic Models

to be announced


Dynamic modelling plays a crucial role in life science research and a key feature of models is parameter uncertainty, arising from biological variation, or a lack of knowledge. It is generally hard to foresee how parameter uncertainty results in variation in the model predictions, especially when a model contains a lot of complex interactions. Some predictions may be sharp, others highly uncertain. Some parameter uncertainties make all predictions uncertain, whereas others may have no influence at all. The purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty coming from uncertain parameter values. We introduce dynamic input-state-output systems and methods to write your model in this format. Also methods are presented to obtain parameter uncertainty from input-output data in case some system states are not measurable, and in case of noise in the data. The methodology is illustrated with realistic examples.


Each day consists partly of lectures and partly of hands-on training. Exercises will be carried out in R.

  • Day 1: uncertainty: terminology and concepts, intro statistics, uncertain systems modelling, state space models, error propagation (Dr. Karel Keesman, Dr. Simon van Mourik)
  • Day 2: sampling based sensitivity analyses, random sampling and Latin hypercube sampling from probability distributions: normal, lognormal, gamma, beta and uniform (Ir. Saskia Burgers)
  • Day 3: work in teams on the first steps of a sensitivity and uncertainty analysis for your own model or a case study (Ir. Saskia Burgers, Dr Simon van Mourik)
  • Day 4: analysis of model output (based on sample), uncertainty analysis, top and bottom marginal variance, i.e. first order sensitivity index (Ir. Saskia Burgers)
  • Day 5: estimating (updating) the parameters and their uncertainty when (new) data come available, Bayesian analysis, adaptive Markov Chain Monte Carlo (MCMC), making predictions with their uncertainty from the MCMC output (Prof. Cajo ter Braak)
General information
Target Group The course is aimed at PhD candidates and other academics
Group Size Min. 15, max. 25 participants
Course duration 5 days
Language of instruction English
Frequency of recurrence Every two years
Number of credits 1.5 ECTS
Lecturers Prof. Cajo ter Braak and Ir. Saskia Burgers (Plant Research International, Wageningen UR), Dr. Simon van Mourik (Farm Technology group, Wageningen University), Dr. Karel Keesman (Biomass Refinery and Process Dynamics group, Wageningen University)
Prior knowledge Basic knowledge of mathematics and statistics
Location Wageningen University Campus
More information

Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116
Email: claudius.vandevijver@wur.nl

Lennart Suselbeek (PE&RC)
Phone: +31 (0) 317 485426
Email: lennart.suselbeek@wur.nl

Registration of interest

At this moment, this course is not scheduled yet. However, if you register your interest in this activity below, we will inform you as soon as the course is scheduled and registration of participation is opened.