  This paper presents a Markov chain Monte Carlo method to generate approximate
posterior samples in retrospective multiple changepoint problems where the
number of changes is not known in advance. The method uses conjugate models
whereby the marginal likelihood for the data between consecutive changepoints
is tractable. Inclusion of hyperpriors gives a near automatic algorithm
providing a robust alternative to popular filtering recursions approaches in
cases which may be sensitive to prior information. Three real examples are used
to demonstrate the proposed approach.
