Abstract
This is the first of two articles which apply certain principles of inference to a practical, financial question. The present article argues and cites arguments which contend that decision making should be Bayesian, that classical (R. A. Fisher, Neyman-Pearson) inference can be highly misleading for Bayesians as can the use of diffuse priors, and that Bayesian statisticians should show remote clients with a variety of priors how a sample implies shifts in their beliefs. We also consider practical implications of the fact that human decision makers and their statisticians cannot fully emulate Savage's rational decision maker.
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Rutgers University
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Markowitz, H.M., Usmen, N. The likelihood of various stock market return distributions, part 1: Principles of inference. J Risk Uncertainty 13, 207–219 (1996). https://doi.org/10.1007/BF00056153
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DOI: https://doi.org/10.1007/BF00056153