College of Mathematics and System Science,
Shenyang Normal University

In some biological experiments, it is
quite common that laboratory subjects may be different in their
patterns of susceptibility to a treatment. We need to determine the
different patterns of susceptibility. In this paper we model the
number of susceptibility's patterns and the parameters jointly, and
base inference about these quantities on their posterior
probabilities, making use of reversible jump Markov chain Monte
Carlo methods that are capable of jumping between the parameter
subspaces corresponding to different numbers of components in the
mixture. For convenience, we always assume different patterns of
susceptibility have common variances. The paper apply the
methodology to the analysis of univariate normal mixtures with
different variances. The practical significance of the proposed
method is illustrated with a dose-response data set.