A limitation of the new index is that it requires more computation to estimate the expected incidence.
However, as we have an additional tuning parameter λ4, it requires more computation to determine optimal tuning parameters using crossvalidation procedure.
Meanwhile, the need for more computations continues to grow.
In terms of the number of multiplications, the AW-XWVD requires approximately 4 times more computations compared to the S-transform and 2,000 times more for the conventional detector.
Compared to the conventional DSR algorithms, the proposed CDSR is an effective and quantifiable algorithm to allocate more computation budget to the blocks with high PMV deviations (such as motion object boundary), and less computation budget to the well-matched motion predicted blocks, while maintaining a constrained computation requirement.
The reason for this is that there are already have more than 19,000 events of MJ = 4.0 in the catalog, and if the magnitude threshold is lowered, the number of selected events increases vastly and it takes much more computation time to fit the model to the data, since the computation time is approximately proportional to the square of the number of events.
In addition, GPU algorithms must be designed with this limitation in mind, and in many cases, may be required to perform significantly more computations, in order to avoid using additional memory [18, 19].
We do not show the results of Gibbs sampling, since (as observed above for both the evolutionary and small-scale hGH data) it requires more computation time to achieve accurate results comparable to those of BP.
However, the algorithm requires more computation time to converge.
