We conducted all BEAST analyses without the data in order to estimate the mismatch between the specified and effective calibration priors, but also the effect of data on posterior distributions.
We showcase that our quantified locality measure can be used to evaluate compiler optimizations, to analyze the locality at different levels of memory hierarchy, to optimize the cache architecture to effectively leverage the locality, and to examine the effect of data prefetching mechanisms.
Vaccine effectiveness estimates were also calculated using the unadjusted coverage data to examine the effect of data cleaning.
Fig. 4 The effect of data volume on other indicators.
The effect of data compression and/or aggregation would render less energy consumption.
Due to the effect of data hiding, pixel oversaturation in plaintext domain could be caused.
Effect of data distribution In Fig. 8, we study the effect of data distribution on algorithms.
Mathew and Vivekanandan (2009) examined the effect of data length on water resource assessment.
Fig. 3 The effect of data preprocessing of the warning model.
The last point might have the effect of data being strongly biased by contextual events.
The effect of data multiplexing on the bandwidth of inductive links is described.
