Model 2


 /I- A Model 1
 tA~



 2 ......... ..................................



Figure 2-2 Segmentation example with instantaneous error: black is signal to be
 predicted, red and blue are two models, the dotted line below is segmentation.

 The criterion used is the instantaneous mean squared error E (n), computed with a

recursive estimate on the instantaneous squared error e (n), using a gamma delay (2.8).

The parameter Yep is the inverse of the memory depth of the filter in samples [13], and

taking the expected values on both sides of (2.8) shows that E[] = E[e2], or p is an

unbiased estimate of estimate of the MSE.

 EP(n)= ye 2(n)+(1 Yp)E (n- ) 0< <1 (2.8)

 Now yp, is another parameter to set, effectively controlling the "window" over

which the MSE is calculated. If the memory depth of e (n) is too short, it is not really an

average anymore, and some spurious switching might appear; if it is too long, the error is

integrated over too much data, and the change detection might not be as sharp. In fact, the

E (n) has the regretful property of building up when the instantaneous error is high

(when the expert is not a winner), and often the decrease in criterion of the expert that