Concepts of Information Based on Utility

The central topic of this paper is the measurement of the amount of information about some parameter o that is present in a set of data X. The parameter o can be any quantity such that a decision maker (DM) is uncertain about its value. We follow a Bayesian approach and assume that the DM can represent his uncertainty at any stage of the learning process in terms of a subjective probability distribution over the parameter space Ω of all possible values of o. This distribution, in turn, will be represented by a generalized probability density function (gpdf) ξ with respect to some fixed σ-finite measure λ on Ω.