Unfortunately, detecting the intervals associated with all the natural frequencies (strong frequency interval detection problem), cannot rely on this property, so that one must resort to Monte Carlo methods or numerical optimization to find them.
Our data indicate a very low probability of interval detection of a type-specific oral HPV infection if the type was not detected at a six-month sampling interval.
Traditional methods for RT interval detection necessitate the detection of either T-wave apexes or offsets.
We face what we call the weak frequency interval detection problem of determining the smallest interval which includes all possible frequencies.
This technique could enroll as depth interval detection in those layers which have more permeability regarding immediate alterations in the well flow logs.
Type-specific infections persistent under a six-month sampling interval had a high probability (0.93, 95%CI 0.83 0.98) of detection at 50% or more of the intervening visits and infections that were absent had a high probability (0.94, 95% CI 0.93 0.95) of no interval detection.
