CORDIS Project
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This research focuses on improving the methods of scientific inference using imprecise probabilities. By developing new scoring rules, the project aims to enhance the application of imprecise probability theory in high-dimensional models, particularly in areas with limited data.
Scientific inference is principally a matter of using observable data to estimate the parameters of models of interest, e.g., models of the climate system.
In traditional Bayesian statistics, uncertainty about model parameters is quantified using a single, precise probability distribution.
This approach has proved extremely successful in applications where data is plentiful and model parameters are few.
But many models are high dimensional (thousands of parameters), and relevant data is comparat…
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