A MAP solution to off-line segmentation of signals

A new criterion for off-line segmentation of signals is proposed. The derivation is general in the sense that it is valid for signals that are parameterized by linear or nonlinear functions embedded in additive noise, be it non-white or non-Gaussian. In addition, a penalty function is developed whose terms are easily justified and interpreted. As a special case, a criterion for segmentation of polynomial signals in white Gaussian noise is analyzed and compared with the AIC and MDL. The simulation results show that our criterion markedly outperforms its popular counterparts.