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.
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© 1986 D. Reidel Publishing Company
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DeGroot, M.H. (1986). Concepts of Information Based on Utility. In: Daboni, L., Montesano, A., Lines, M. (eds) Recent Developments in the Foundations of Utility and Risk Theory. Theory and Decision Library, vol 47. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4616-3_17
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DOI: https://doi.org/10.1007/978-94-009-4616-3_17
Publisher Name: Springer, Dordrecht
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