Journal of Mathematical Psychology volume 49, issue 3, P205-225 2005 DOI: 10.1016/j.jmp.2005.02.004 View full text
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Jay I. Myung, George Karabatsos, Geoffrey J. Iverson

Abstract: Theories of decision making are often formulated in terms of deterministic axioms, which do not account for stochastic variation that attends empirical data. This study presents a Bayesian inference framework for dealing with fallible data. The Bayesian framework provides readily applicable statistical procedures addressing typical inference questions that arise when algebraic axioms are tested against empirical data. The key idea of the Bayesian framework is to employ a prior distribution representing the pa…

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