  In this work we consider time series with a finite number of discrete point
changes. We assume that the data in each segment follows a different
probability density functions (pdf). We focus on the case where the data in all
segments are modeled by Gaussian probability density functions with different
means, variances and correlation lengths. We put a prior law on the change
point instances (Poisson process) as well as on these different
parameters(conjugate priors) and give the expression of the posterior probality
distributions of these change points. The computations are done by using an
appropriate Markov Chain Monte Carlo (MCMC) technique.
  The problem as we stated can also be considered as an unsupervised
classification and/or segmentation of the time serie. This analogy gives us the
possibility to propose alternative modeling and computation of change points,
which are more appropriate for multivariate signals, for example in image
processing.
