Value of Shannon’s information for the most important Bayesian systems

Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe and Ruslan L. Stratonovich
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Roman V. Belavkin: Middlesex University, Faculty of Science and Technology
Panos M. Pardalos: University of Florida, Industrial and Systems Engineering
Jose C. Principe: University of Florida, Electrical & Computer Engineering

Chapter Chapter 10 in Theory of Information and its Value, 2020, pp 327-352 from Springer

Abstract: Abstract In this chapter, the general theory concerning the value of Shannon’s information, covered in the previous chapter, will be applied to a number of important practical cases of Bayesian systems. For these systems, we derive explicit expressions for the potential Γ(β), which allows us to find a dependency in a parametric form between losses (risk) R and the amount of information I and then, eventually, to find the value function V (I).

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-22833-0_10

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DOI: 10.1007/978-3-030-22833-0_10

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