To obtain a better approximation of the sampled counterpart model to the real stochastic structure of the demand, it would be desirable to apply variance reduction techniques such as Importance Sampling, and to switch to a variable-sample approach which allows the consideration of a much larger set of random scenarios without compromising runtime too much.
Even though they were randomly selected from a much larger list, a purely random sample could have been more appropriate.
Using a more stringent SCD definition of a sequence containing at least 3 S/T-Q in a stretch of 50 amino acids, we arrived at a refined census of 436 proteins in the yeast proteome, still a much larger number than expected at random.
Furthermore, generation of random sequences (TTS and no-TTS) showed that no-TTS random sequences have a much larger probability to be transcription factor binding sites than TTS random sequences.
However, we observe a much larger absolute number of non-random ORFs than predicted based on known annotated proteins.
We are also confident that random perturbations using the unified breakpoint profile (UBP) provide a much more realistic background (null) model than random perturbations of a much larger number of (linked) individual probe data points.
Interestingly, at high WNRs, random LUTs can withstand a much larger coalition than Tardos codes.
This indicates to us that there are still some fragile item factor associations in the six-factor model, otherwise this comparison would yield a much larger reduction in BIC when comparing theoretical to random models.
Driving process: The properties of Lemma 2.1 are not only maintained by Gaussian random processes but also by a much larger class of driving processes.
Though this search strategy reduces the amount of tree space explored for any given random addition sequence replicate, it allows for a much larger number of bootstrap replicates to be performed.
