Day 1 will start at mid-day to allow for travel from other cities. Day 5 will finish at mid-day
Online or UKCEH Edinburgh, Bush Estate, Penicuik, Midlothian, EH26 0QB, Location map & travel directions
We are considering a conversion to an online course . We are hoping to set a new date in spring 2020 for autumn 2020.
£ 799 students (Early Bird Rate, then £849)
£1049 non-students (Early Bird Rate, then £1199)
This includes the cost for refreshments, but excludes the cost for evening meals and accommodation.
Short Course Description:
This four-day course will run over 5 days. It will be a mixture of presentations and practical exercises. It will give you a solid grounding in Bayesian methods that can be used with any kind of model and data set to compare models, estimate parameters, analyse uncertainties and communicate results.
We will cover:
Day 1: (half-day): Bayesian basics & overview of the course
Day 2: Hierarchical modelling, Spatio-temporal modelling
Day 3: Comprehensive uncertainty quantification, Combining disparate datasets
Day 4: : Model comparison, Communication of results
Day 5: (half-day): Software choices, Graphical modelling, Discussion of the future of Bayesian methodology
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 allocated to practical exercises where you can opt to use the materials provided by the teachers, or use your own model(s) and data.
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 (JAGS, BayesianTools)
- Know how to use R to write code for Bayesian models not covered by existing software
- Design and apply simple Bayesian statistical models for any data set
- Design and apply hierarchical models
- Know how to combine disparate data sets in a single analysis
- Carry out Bayesian parameter calibration of complex process-based models
- Understand stochastic emulators and be able to apply them for low-dimensional inputs and outputs
- Compare the relative plausibility of different models by means of Bayes’ Factors or simple information criteria (AIC, BIC etc.)
- Comprehensively analyse uncertainties associated with model outputs
- Communicate uncertainties clearly
Previous Learners 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!"
( a professional participant, Sep 2019)
Accommodation is not included in the cost. We will advise on suitable accommodation near the venue at an appropriate time.
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 reading listed below:
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.
Marcel van Oijen, Ecosystem modeller, UKCEH. Marcel has >15 years of experience in the application of Bayesian methods in environmental and ecological research. He has given lectures and tutorials on the methods in Finland, France, Portugal, the U.K. and elsewhere.
Dr Kate Searle, Ecological Modeller, UKCEH. Kate has 15 years of experience in leading research focusing on the effects of environmental change on wildlife behaviour, populations and distributions. By combining contemporary ecological techniques with an understanding of resource-consumer dynamics in heterogeneous environments, she aims to gain mechanistic, process-driven understanding of ecological systems through both statistical modelling and more applied, management-orientated experiments. Her research specialises in methods for scaling up from the mechanisms underlying individual behaviour of wildlife to understand and predict population and ecosystem-level consequences in rapidly changing, complex environments.
David Cameron, Ecosystem modeller, UKCEH. David has >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.
MSc and PhD students, early career researchers, academics, CDT and DTP students
Recommended reading prior to 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 teachers 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.
Van Oijen, M. (2017). Bayesian methods for quantifying and reducing uncertainty and error in forest models. Current Forestry Reports 3: 269-280. https://doi.org/10.1007/s40725-017-0069-9 .
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 .
Roer Hjelkrem, A.-G., Höglind, M., Van Oijen, M., Schellberg, J., Gaiser, T. & Ewert, F. (2017). Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments. Ecological Modelling 359: 80-91. https://doi.org/10.1016/j.ecolmodel.2017.05.015 .
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 .
Van Oijen, M. & Höglind, M. (2016). Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design. Euphytica 207: 627-643. DOI 10.1007/s10681-015-1562-5. http://link.springer.com/article/10.1007%2Fs10681-015-1562-5 .
Searle, Kate R; Rice, Mindy B; Anderson, Charles R; Bishop, Chad; Hobbs, NT; 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia 179(2):377-391
Stephen Freeman, Kate Searle, Maria Bogdanova, Sarah Wanless & Francis Daunt. 2014. POPULATION DYNAMICS OF FORTH & TAY BREEDING SEABIRDS: REVIEW OF AVAILABLE MODELS AND MODELLING OF KEY BREEDING POPULATIONS Ref: MSQ – 0006. Report to Marine Science Scotland. https://www2.gov.scot/resource/0044/00449072.pdf.
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.
Fu, Y.H., Campioli, M., Van Oijen, M., Deckmyn, G. & Janssens, I.A. (2012). Bayesian comparison of six different temperature-based budburst models for four temperate tree species. Ecological Modelling 230: 92-100.
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.
Burthe, Sarah; Butler, Adam; Searle, Kate R; Hall, Stephen JG; Thackeray, Stephen J; Wanless, Sarah; 2011. Demographic consequences of increased winter births in a large aseasonally breeding mammal (Bos taurus) in response to climate change. Journal of Animal Ecology 80(6): 1134-1144
Van Oijen, M. & Thomson, A. (2010). Towards Bayesian uncertainty quantification for forestry models used in the United Kingdom Greenhouse Gas Inventory for land use, land use change, and forestry. Climatic Change 103: 55-67.
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.
Yeluripati, J.B., Van Oijen, M., Wattenbach, M., Neftel, A., Ammann, A., Parton, W.J. & Smith, P. (2009). Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models. Soil Biology and Biochemistry 41: 2579-2583.
Lehuger, S., Gabrielle, B., Van Oijen, M., Makowski, D., Germon, J.-C., Morvan, T. & Hénault, C. (2009). Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model. Agriculture, Ecosystems and Environment 133: 208-222.
Reinds, G.J., Van Oijen, M., Heuvelink, G.B.M. & Kros, H. (2008). Bayesian calibration of the VSD soil acidification model using European forest monitoring data. Geoderma 146: 475-488.
Patenaude, G., Milne, R., Van Oijen, M., Rowland, C.S. & Hill, R.A. (2008). Integrating remote sensing datasets into ecological modelling: a Bayesian approach. International Journal of Remote Sensing 29: 1295-1315.
Van Oijen, M., Rougier, J. & Smith, R. (2005). Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiology 25: 915-927.