Detecting change points and monitoring biomedical data

Abstract

Bayesian and likelihood approaches to on-line detecting change points in time series are discussed and applied to analyze biomedical data. Using a linear dynamic model, the Bayesian analysis outputs the conditional posterior probability of a change at time t − 1, given the data up to time t and the status of changes occurred before time t − 1. The likelihood method is based on a change-point regression model and tests whether there is no change-point.