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).
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Belavkin, R.V., Pardalos, P.M., Principe, J.C., Stratonovich, R.L. (2020). Value of Shannon’s information for the most important Bayesian systems. In: Belavkin, R., Pardalos, P., Principe, J. (eds) Theory of Information and its Value. Springer, Cham. https://doi.org/10.1007/978-3-030-22833-0_10
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DOI: https://doi.org/10.1007/978-3-030-22833-0_10
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22832-3
Online ISBN: 978-3-030-22833-0
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