How can we improve the prediction of systemic risks in financial and economic crises? A new research project is developing innovative Bayesian methods to model dynamic covariances – with the aim of improving forecasts and supporting political decisions.
The European Union has set ambitious targets for the agricultural sector with the Green Deal until 2030. To achieve these, land use and land management need to be regulated in a finely tuned way. Alexander Mozdzen, PhD student at the Department of Statistics at the University of Klagenfurt, in collaboration with Tamás Kristin (IIASA) and Gregor Kastner (University of Klagenfurt), has developed an innovative Bayesian space-time model to analyse the impact of agricultural subsidies on land use in Europe. The model aims to provide a clearer assessment of which policies have been effective in regulating land use.
Wind, sun and water do not produce constant amounts of energy. What’s more, renewable energy is difficult to store. Michaela Szölgyenyi is working on mathematical methods that can be used, for example, to better predict how much electricity a solar power plant will most likely produce at any given time.
Lukas Sommeregger’s research focuses on developing methods to determine the lifespan of computer chips. The doctoral student, who also works at Infineon, recently received the Infineon Innovation Award.
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