Learner feedback in March 2024 on the NERC-funded courses were 91% positive.
Location:
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
Cost:
We are exploring a number of options to continue this training offer beyond March 2024. This includes a paid-for face-to-face course.
Face-to-face course from £999 (tbc)
Please express your interest here so we can secure a date!
Date:
Early 2025
The face-to-face courses run from 1 pm on Monday to 1 pm on Friday.
Short course description:
This interactive 5-day 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
- Understand scientific papers that use Bayesian methods and terminology
- Know how to use Bayesian software
- Design and apply simple Bayesian statistical models
- Compare the relative plausibility of different models
- Comprehensively analyse uncertainties associated with model outputs
Target audience:
- PhD students
- Early career researchers
- Academics/ researchers, including those from industry or the charitable sector
Level:
Intermediate
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:
Places:
16
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.
Accommodation:
The cost of accommodation is not included in the course fee.
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:
Learner feedback in March 2024 on the NERC-funded courses were 91% positive.
“The course has given me a lot of tools which can directly help to solve some of the issues I've been facing in my PhD.” (learner, March 2024)
“The course leaders were very responsive and happy to answer questions.” (learner, March 2024)
“The basics were very well explained which really helped with the more advanced topics. I also liked that we were able to work through examples as well as listen to the theory.” (learner, March 2024)
“I really enjoyed every part of the course. The best thing about it was the structure itself: that we were given a very thorough overview of the principles at the basis of the methodology, then progressing to more complicated applications” (learner, March 2024)
"I enjoyed the interactive walkthroughs using R. It was nice to be able to apply the lectures to the practical directly." (learner, January 2024)
"I liked the practicals where we then went though the rationale behind them, and spent more time on what exactly the plots are showing and what the model results mean." (learner, January 2024)
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.