Statistical Default Inference Based on DFL

Statistical default logic is an expansion of classical (i.e. Reiter´s) default logic that allows us to model common inference patterns found in standard inferential statistics, which is an expansion with an error-bound parameter. This paper proposes statistical default inference based on dynamic fuzzy logic (DFL) and constructs a method of computing extensions of fuzzy statistical default theory.