Statistical 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. 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 and sensitivity coming from uncertain parameter values. Methods are presented to obtain parameter uncertainty noisy measurement data. The methods are 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, sampling based sensitivity analyses, random sampling and Latin hypercube sampling from probability distributions: normal, lognormal, gamma, beta and uniform
  • Day 2: analysis of model output (based on sample), uncertainty analysis, sensitivity analysis, top and bottom marginal variance, i.e. first order sensitivity index
  • Day 3: work in teams on the first steps of a sensitivity and uncertainty analysis for your own model or a case study
  • Day 4: uncertainty modelling, dynamic models, error propagation, estimating (updating) the parameters and their uncertainty when (new) data come available, Bayesian analysis
  • Day 5: Markov Chain Monte Carlo (MCMC), making predictions with their uncertainty from the MCMC output. Presentation of results of teamwork
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 Ir. Saskia Burgers (Biometris, Wageningen UR) and Dr. Simon van Mourik (Farm Technology group, Wageningen University)
Prior knowledge Participants are expected to have a good knowledge of basic statistics (like hypothesis testing, t- and F-tests and linear regression) and some experience in a statistical package (GenStat, R, SPSS, or likewise)
Location Wageningen University Campus
More information

Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116

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.