Details of Award
NERC Reference : NE/P020631/1
Bayesian methods to quantify uncertainty and risk in the Environment
Training Grant Award
- Lead Supervisor:
- Professor C Robertson, University of Strathclyde, Mathematics and Statistics
- Grant held at:
- University of Strathclyde, Mathematics and Statistics
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Atmospheric
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Earth & environmental
- Population Ecology
- Ecosystem Scale Processes
- Statistics & Appl. Probability
- Ecosystem Scale Processes
- Abstract:
- The NERC Probability, Uncertainty and Risk in the Environment (PURE) action aims to improve the understanding of uncertainty and risk by developing new methods that will provide more accurate risk assessments to scientists, governments and businesses. The understanding of Bayesian methodologies is therefore key to the design and implementation of effective methodologies to capture uncertainty and quantify risk. The understanding of risk, uncertainty and the associated methodologies is often extremely limited in the NERC postgraduate community. Our programme seeks to address this gap in knowledge by delivering a tailored, 4-day residential master-class in Bayesian methods for uncertainty and risk that draws on the multidisciplinary expertise in the University of Strathclyde Department of Mathematics and Statistics. Using examples from environmental science throughout, Bayesian model fitting methods will be introduced and compared to classical approaches. We will demonstrate how to set up complex structural models where Bayesian methods are necessary. Extensions to spatial and temporal smoothing, for example in disease mapping, will also be covered. The OpenBUGS statistical package will be used in conjunction with R for practical sessions. The focus will be on applications and modelling, particularly in areas cognisant with the students' research, but will also cover sufficient theory to explain the modelling concepts. We assume some previous exposure to elementary statistics. On completion of this course students will have a good understanding of the role of Bayesian methods in statistical modelling and will be aware of the advantages and disadvantages of this modelling in relation to traditional statistical modelling. They will also go away with working computer code which they can modify and reuse in their own work. They will also be able to critically review and assess Bayesian modelling reported in the environmental science literature.
- NERC Reference:
- NE/P020631/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £23,128
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £23,128 |
If you need further help, please read the user guide.