In other words, and must be decided beforehand in order to set the thresholds appropriately. The numerical value will depend on the application. The reason for using approximation signs is that, in the discrete case, the signal may cross the threshold between samples. Thus, depending on the penalty of making an error and the sampling frequency, one might set the thresholds more aggressively. Of course, the exact bounds may be used in the continuous case.  Holger Wilker: Sequential-Statistik in der Praxis, BoD, Norderstedt 2012, . External links R Package: Wald's Sequential Probability Ratio Test  by Stéphane Bottine 
In other words, and must be decided beforehand in order to set the thresholds appropriately. The numerical value will depend on the application. The reason for being only an approximation is that, in the discrete case, the signal may cross the threshold between samples. Thus, depending on the penalty of making an error and the sampling frequency, one might set the thresholds more aggressively. The exact bounds are correct in the continuous case. Further reading Holger Wilker: Sequential-Statistik in der Praxis, BoD, Norderstedt 2012, . External links Wald's Sequential Probability Ratio Test for R by Stéphane Bottine Wald's Sequential Probability Ratio Test for Python by Zhenning Yu
R
using approximation signs
being only an approximation
R
Of course, the exact bounds may be used
The exact bounds are correct
A
Further reading
D
R Package:
A
for R
A
Wald's Sequential Probability Ratio Test for Python by Zhenning Yu