ERC Starting Grant · 2019
Epistemic Utility for Imprecise Probability
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 comparatively sparse. In such contexts, imprecise probabilities are required to adequately capture uncertainty. The mathematical foundations of imprecise probability theory (IP) have been in place for 25…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.