The uncertainty of classification in discriminant analysis may result from
the original characteristics of the phenomena studied, the approach of infe
rring population parameters, and the credibility of the parameters which ar
e estimated by geologist or statistician. A credibility function and a sign
ificance function are proposed. Both can be used to appraise the uncertaint
y of classification. The former is involved with the uncertainty resulting
from the errors in the reward-penalty matrix, while the latter may be invol
ved with the uncertainty resulting from the original characteristics of the
phenomena studied and the statistical approach. Inappropriate classified r
esults may be originated from the bias estimates of population parameters (
mean vector and covariance matrix), which are estimated by bias samples. Th
ese bias estimates can be updated by constraining the varying region of the
mean vector The equations for updating Bayesian estimates of the mean vect
or and the covariance matrix are demonstrated if the mean vector is restric
ted to a subregion of the entire real space. Results for a gas reservoir in
dicate that the discriminant rules based on the updated equations are more
efficient than the traditional discriminant rules.