Testing for ontological errors in probabilistic forecasting models of natural systems.

ABSTRACT: Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed

SUBMITTER: Marzocchi W 

PROVIDER: S-EPMC4143071 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature