Item

Abstract

Default reasoning about probabilities is the assignment of subjective
probabilities
on the basis of partial information about an uncertain event, and statistical
knowledge about the domain of discourse. In this thesis a logic is developed
for representing
probabilistic information about both statistical and subjective probabilities.
The question
is explored about how exactly statistical knowledge should influence the
formation of
degrees of belief. Strong arguments are presented that justify Cross-Entropy
minimization as
the appropriate rule of inference. The minimum cross-entropy principle is
implemented in a
preferred model semantics for the representation language. When probabilities
are allowed to
take values in real closed fields with a logarithmic function a complete proof
system for
the resulting logic is obtained.