Retrospective Changepoint Detection

The problem of detecting and estimating the location of changepoints (or discontinuities) in data is fundamental to many areas of data analysis. Practical applications abound in diverse areas such as medicine (e.g. monitoring of drug levels in hospital patients), the detection of kickback in oil well pressure data [140] and edge detection in images [123]. In this chapter, optimal Bayesian techniques are developed for changepoint identification in one dimensional (time series) data. These use the probability density function (pdf) of the changepoint positions to estimate their positions in time series.