Bayesian Retrospective Multiple-Changepoint Identification

Abstract

Changepoint identification is important in many data analysis problems, such as industrial control and medical diagnosis-given a data sequence, we wish to make inference about the location of one or more points of the sequence at which there is a change in the model or parameters driving the system. For long data sequences, however, analysis (especially in the multiple-changepoint case) can become computationally prohibitive, and for complex non-linear models analytical and conventional numerical techniques are infeasible. We discuss the use of a sampling-based technique, the Gibbs sampler, in multiple-changepoint problems and demonstrate how it can be used to reduce the computational load involved considerably