  This paper proposes a new minimum description length procedure to detect
multiple changepoints in time series data when some times are a priori thought
more likely to be changepoints. This scenario arises with temperature time
series homogenization pursuits, our focus here. Our Bayesian procedure
constructs a natural prior distribution for the situation, and is shown to
estimate the changepoint locations consistently, with an optimal convergence
rate. Our methods substantially improve changepoint detection power when prior
information is available. The methods are also tailored to bivariate data,
allowing changes to occur in one or both component series.
