The Statistics Department has recently been awarded funding from NERC for the project "Statistical inference and uncertainty quantification for complex process-based models using multiple data sets". Principal Investigator Richard Everitt, will collaborate on the project with other members of the Department (Rito Dutta, Christian Robert and Martyn Plummer), the Ecology group at the University of Reading, and with the Centre for Environment, Fisheries and Aquaculture Science.

Making responsible decisions about landscapes is facilitated by the use of complex models able to represent multiple competing demands on land use. Decisions about land use require that trade-offs between competing demands be identified, and their consequences through time be characterised. Models consisting of stochastic computer simulations are increasingly used to make realistic predictions about real world processes from socio-ecological systems involving land use. dels attempt to simulate all relevant aspects of a real physical system, they may involve many parameters, some of which will be difficult to set correctly. The final objective of these models is to assess the possible consequences of management decisions, such as the placement of wind turbines, thus it is crucially important that the uncertainty introduced by calibrating parameter values be understood.

In order to make informed decisions, one needs to be able to consider the effects of a number of complex interacting temporal and spatial processes (e.g. hydrological, ecological, agricultural, economic, climate). The project will develop new techniques in Approximate Bayesian Computation to enable parameter estimation for models for these processes, taking into account the impact of model misspecification. This project is part of the Strategic Priorities Fund on Landscape Decisions. https://landscapedecisions.org/