Face to face
Edinburgh & Wallingford


Free! Apply now!


22-26 January and 18-22 March 2024

Your current PhD/ work role must be funded by NERC, at least in part, to be eligible. Please make sure you understand the application scoring criteria before submitting your application! 


UKCEH Edinburgh, Bush Estate, Penicuik, Midlothian, EH26 0QB, Location map & travel directions

UKCEH Wallingford, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB Location map & travel directions



We are grateful for the funding received from the Natural Environment Research Council to facilitate offering this training for free.

This will include the provision of accommodation. We will also reimburse you for travel  (see details below).


22-26 January 2024, Edinburgh

18-22 March 2024, Wallingford, nr. Oxford

The courses will run from 1 pm on Monday to 1 pm on Friday.

There will be one interactive online follow-up workshop after each event on Thu 29 February, at 10 am and Wed 24 April, at 10 am. Attendance is optional, but highly recommended.

Both locations are suitable for wheelchair users and we have disabled toilets throughout the building.

Application and selection criteria

We expect the training to be oversubscribed. We are using the application survey and scoring criteria to ensure a fair and transparent allocation of available places.

To attend, you will need to submit an application. 

UKCEH is fully committed to Diversity, Equity and Inclusion. Please find more detail in the UKCEH Equality, Diversity & Inclusion Policy. We are inviting applications from a wide range of candidates with different backgrounds. Please state any special requirements or an impairment we need to be aware of during the application process. We can then explore any reasonable adjustments we can make (e.g. transport to the training venue by car, avoidance of allergens etc)

Your current PhD/ work role must be funded by NERC, at least in part, to be eligible. Please make sure you understand the application scoring criteria before submitting your application! 

Apply now: Application form 

Short course description:

This interactive course will be a mixture of presentations and practical exercises. It will give you a solid grounding in Bayesian methods that you can use with any kind of model and data set to compare models, estimate parameters, analyse uncertainties and communicate results.

The course will use examples of models and data from natural environment research. However, you will be able to transfer the methods and techniques to any other model, data set or research question. There will be time during practical exercises where you can opt to use the materials provided by the trainers, or use your own model(s) and data.

Learning objectives:

By the end of the course, learners will be able to

  1. Understand scientific papers that use Bayesian methods and terminology
  2. Know how to use Bayesian software 
  3. Design and apply simple Bayesian statistical models
  4. Know how to combine disparate data sets in a single analysis
  5. Compare the relative plausibility of different models 
  6. Comprehensively analyse uncertainties associated with model outputs
  7. Communicate uncertainties clearly

Target audience:

  • PhD students
  • Early career researchers
  • Academics/ researchers, including those from industry or the charitable sector



Learners need to have some practical knowledge of the programming language R [ https://www.r-project.org/about.html ]. No previous exposure to probability theory or Bayesian methods is necessary, but students may want to read some introductory material beforehand. We recommend the reading listed below:


16 per event

Hardware and software requirements:

All data analysis and modelling will use the freely available language R. You will need to bring your own laptop with R installed. We can provide a limited number of laptops (if you only have a desktop machine). We can provide guidance on how to install R on your machine. Our Bayesian analyses will mostly use the freely available software JAGS (which can be called from R) and some self-written code. The course leaders will prepare R-scripts beforehand. We will briefly illustrate some alternative software to JAGS, including BUGS and INLA.

Travel & accommodation:

We will provide accommodation, where required. We will reimburse you for your travel costs as outlined in our reimbursement policy. We expect you to book your travel as soon as we have confirmed your place to keep the cost as low as possible.

Course leader:

Peter Henrys, Statistician, UKCEH

Other trainers:

David Cameron, Ecosystem modeller, UKCEH. David has over 13 years of experience in the application of Bayesian methods in environmental and ecological research. For the last three years he has been one of the lecturers at the spring school of COST Action PROFOUND “Bayesian calibration, forecasting and multi-model predictions of process-based vegetation models” in Grenoble, France (http://cost-profound.eu/site/event/3rd-profound-spring-school-bayesian-calibration-forecasting-and-multi-model-predictions-of-process-based-vegetation-models-in-rencurel-grenoble-france-5-10-of-may/)

Peter Levy, Ecosystem modeller, UKCEH. Peter has worked on plant physiology, carbon sequestration, and the exchanges of greenhouse gases between ecosystems and the atmosphere. In recent years, he has applied Bayesian approaches to the analysis of these data, for example in estimating the uncertainty in cumulative gas emissions.

Lindsay Flynn Banin, Statistical Ecologist, UKCEH

Previous course participants said:

"Well-considered course structure and content – interactive and friendly with a relaxed teaching style that meant we could easily discuss issues along the way.Very good at explaining the Bayesian approach and how it can be applied. Also – fantastic resources and R code provided via GitHub!"

(learner, September 2019)

Recommended reading before the course:

1) Hartig et al. (2012. J. Biogeogr. 39: 2240-2252; https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1365-2699.2012.02745.x)

2) Van Oijen (2017). Curr. For. Rep. 3: 269-280; https://doi.org/10.1007/s40725-017-0069-9   and http://nora.nerc.ac.uk/id/eprint/518404/

Selected publications by the trainers that make use of Bayesian methods:

Levy, P., Van Oijen, M., Buys, G. & Tomlinson, S. (2018). Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach. Biogeosciences 15: 1497-1513. https://doi.org/10.5194/bg-15-1497-2018.

Bagnara, M., Van Oijen, M., Cameron, D., Gianelle, D., Magnani, F. & Sottocornola, M. (2018). Bayesian calibration of a simple forest model with a multiplicative mathematical structure: a case study with a Light Use Efficiency model in an alpine forest. Ecological Modelling 371: 90-100. https://doi.org/10.1016/j.ecolmodel.2018.01.014.

Levy, P., Cowan, N., Van Oijen, M., Famulari, D., Drewer, J. & Skiba, U. (2017). Estimation of cumulative fluxes of nitrous oxide: uncertainty in temporal upscaling and emission factors. Eur. J. Soil Sci. 68: 400-411. https://doi.org/10.1111/ejss.12432 .

Höglind, M., Van Oijen, M., Cameron, D. & Persson, T. (2016). Process-based simulation of growth and overwintering of grassland using the BASGRA model. Ecological Modelling 335: 1-15. https://doi.org/10.1016/j.ecolmodel.2016.04.024 .

Reyer, C., Lasch, P., Flechsig, M. & Van Oijen, M. (2016). Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity. Climatic Change 137: 395-409. https://doi.org/10.1007/s10584-016-1694-1 .

Minunno, F., Van Oijen, M., Cameron, D. & Pereira, J.S. (2013). Selecting parameters for Bayesian calibration of a process-based model: a methodology based on canonical correlation analysis. J. Uncertainty Quantification 1: 370–385 < http://dx.doi.org/10.1137/120891344>.

Cameron, D.R., Van Oijen, M., Werner, C., Butterbach-Bahl, K., Grote, R., Haas, E., Heuvelink, G., Kiese, R., Kros, J., Kuhnert, M., Leip, A., Reinds, G.J., Reuter, H.I., Schelhaas, M.J., de Vries, W. & Yeluripati, J. (2013). Environmental change impacts on the C- and N-cycle of European forests: a model comparison study. Biogeosciences 10: 1751-1773. http://www.biogeosciences.net/10/1751/2013/bg-10-1751-2013.html

Minunno, F., Van Oijen, M., Cameron, D., Cerasoli, S., Pereira, J.S. & Tomé, M. (2013). Using a Bayesian framework and global sensitivity analysis to identify strengths and weaknesses of two process-based models differing in representation of autotrophic respiration. Environmental Modelling & Software 42: 99-115.

Van Oijen, M., Reyer, C., Bohn, F.J., Cameron, D.R., Deckmyn, G., Flechsig, M., Härkönen, S., Hartig, F., Huth, A., Kiviste, A., Lasch, P., Mäkelä, A., Mette, T., Minunno, F. & Rammer, W. (2013). Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe. Forest Ecology and Management 289: 255-268.

Van Oijen, M., Cameron, D.R., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson, P.-E., Kiese, R., Rahn, K.-H., Werner, C., Yeluripati, J.B. (2011). A Bayesian framework for model calibration, comparison and analysis: application to four models for the biogeochemistry of a Norway spruce forest. Agriculture and Forest Meteorology 151: 1609-1621.

Thorsen, S.M., Roer, A.-G. & Van Oijen, M. (2010). Modelling the dynamics of snow cover, soil frost and surface ice in Norwegian grasslands. Polar Research 29: 110-126.


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