Bayesian analysis of autoregressive time series with change points

The paper deals with the identification of a stationary autoregressive model for a time series and the contemporary detection of a change in its mean. We adopt the Bayesian approach with weak prior information on the parameters of the models under comparison and an exact form of the likelihood function. When necessary, we resort to fractional Bayes factors to choose between models, and to importance sampling to solve computational issues.