Noninformative/Informative Bayesian Measures of Probability to Compare
Predictive Accuracy of Estimation Methods in Software Reliability
Abstract
ARE: The Absolute Relative Error and
SRE: The Squared Relative Error, as measurements of predictive accuracy of
predictions of number of software failures for a target mission time
for alternative reliability models is currently an important concern to all
(Sahinoglu, 1992, 1997) The proposed way of quantifying by calculating the
Bayesian probability of how much one method's prediction accuracy is better
(less ARE and/or SRE) than the other alternative is far more realistic than
a) deciding by comparing the mere expected values of ARE and SRE as
conventionally done b)deciding by conducting statistical hypotheses tests
of pairwise means, an approach more sensible than (a). We have first used
noninformative and then informative priors by placing half-normal priors on
the ARE(and SRE) r.v. as they are peaked around the ideal zero percent
relative error. These details are discussed in Berger and Deely(1988) for
the general problem of ranking normal means.