Statistical Uncertainty Analysis of Dynamic Models

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Statistical Uncertainty Analysis of Dynamic Models

9 - 13 December 2019


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 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
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Note that besides the restaurants in Wageningen, there are also options to have dinner at Wageningen Campus.


Fees 1
PE&RC / WIMEK / WASS / EPS / VLAG / WIAS PhD candidates with an approved TSP € 350,- € 400,-
a) All other PhD candidates
b) Postdocs and staff of the above mentioned Graduate Schools
€ 740,- € 790,-
All others € 1.090,- € 1.140,-

1 The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation (NB: options for accommodation are given above)
2 The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 11 NOVEMBER 2019


  • If you need an invoice to complete your payment, please send an email to, including ALL relevant details that should be mentioned on the invoice (e.g., purchase order no., specific addresses, attendees, etc.).
  • The Early-Bird policy is such that the moment of REGISTRATION (and not payment) is leading for determining the fee that applies to you.
  • Please make sure that your payment is arranged within two weeks after your registration.
  • It is the participant's responsibility to make sure that he/she (or his/her secretary) completes the payment correctly and in time.
PE&RC Cancellation Conditions
  • Up to 4 (four) weeks prior to the start of the course, cancellation is free of charge.
  • Up to 2 (two) weeks prior to the start of the course, a fee of € 350,- will be charged.
  • In case of cancellation within two weeks prior to the start of the course, a fee of € 740,- will be charged.
  • If you do not show at all, a fee of € 1.090,- will nevertheless be charged.

Note: If you would like to cancel your registration, ALWAYS inform us (and do note that you will be kept to the cancellation conditions)

More information

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

Dr. Lennart Suselbeek (PE&RC)
Phone: +31 (0) 317 485426


To register, please enter your details below and click "Register".