If (mu, sigma, Sigma) denote the location, scale, and shape parameters of a continuous variate X, we show how the exact likelihood function L(mu, sigma,Sigma\x(1).... x(n)) based on n independent observed values of X can be displayed and used to make frequency-interpretable inferences about any or all of the three parameters. When interest is confined to fewer than three parameters, "simplifying assumptions" may be needed to preserve accuracy in the frequency interpretation. Such simplifying assumptions mathematically resemble Bayesian priors but their logical status is quite different. The approach used leads towards a "Bayes-Frequentist" compromise. (C) 2002 Elsevier Science B.V. All rights reserved
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