Concepts of Information Based on Utility
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TL;DR
This paper follows a Bayesian approach and assumes 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.
Abstract
The central topic of this paper is the measurement of the amount of information about some parameter ö that is present in a set of data X. The parameter ö 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 ö. 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.