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A Conjugate Class of Utility Functions for Sequential Decision Problems
Author(s) -
Houlding Brett,
Coolen Frank P. A.,
Bolger Donnacha
Publication year - 2015
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12359
Subject(s) - simple (philosophy) , class (philosophy) , property (philosophy) , conjugate prior , conjugacy class , bayesian probability , decision theory , computer science , statistical inference , inference , decision problem , bayesian inference , exponential family , mathematical economics , mathematics , mathematical optimization , artificial intelligence , machine learning , bayes' theorem , algorithm , statistics , discrete mathematics , philosophy , epistemology
The use of the conjugacy property for members of the exponential family of distributions is commonplace within Bayesian statistical analysis, allowing for tractable and simple solutions to problems of inference. However, despite a shared motivation, there has been little previous development of a similar property for using utility functions within a Bayesian decision analysis. As such, this article explores a class of utility functions that appear to be reasonable for modeling the preferences of a decisionmaker in many real‐life situations, but that also permit a tractable and simple analysis within sequential decision problems.