Abstract
AbstractWe consider approximate Bayesian inference about the quantity R = P[Y2> Y1] when both the random variables Y1, Y2 have expectations that depend on certain explanatory variables. Our interest centers on certain characteristics of the posterior of R under Jeffreys's prior, such as its mean, variance and percentiles. Since the posterior of R is not available in closed form, several approximation procedures are introduced, and their relative performance is assessed using two real datasets.
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