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

  • Roman V. Belavkin
  • Panos M. Pardalos
  • Jose C. Principe


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).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roman V. Belavkin
    • 1
  • Panos M. Pardalos
    • 2
  • Jose C. Principe
    • 3
  1. 1.Faculty of Science and TechnologyMiddlesex UniversityLondonUK
  2. 2.Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Electrical & Computer EngineeringUniversity of FloridaGainesvilleUSA

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