  We study the use of spike and slab priors for consistent estimation of the
number of change points and their locations. Leveraging recent results in the
variable selection literature, we show that an estimator based on spike and
slab priors achieves optimal localization rate in the multiple offline change
point detection problem. Based on this estimator, we propose a Bayesian change
point detection method, which is one of the fastest Bayesian methodologies, and
it is more robust to misspecification of the error terms than the competing
methods. We demonstrate through empirical work the good performance of our
approach vis-a-vis some state-of-the-art benchmarks.
