This has led to a great interest among researchers in accurate modeling of network traffic.
Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies.
This is a key element in developing accurate models of the Sun's dynamics for space weather.
Although non-linear machine learning algorithms usually results in more accurate models for large descriptive QSAR data sets, a linear method constitutes a baseline.
Investigations of these processes are of importance in developing accurate models for charge-transfer (ionisation) into the surfaces [50, 53, 54].
Cycles of this process will result in more accurate models; ultimately, these models will explain the systems-level properties of the biological system of interest.
In particular, accurate models combine an HRC with odd-ordered correlations that account for statistical light dark asymmetries in the HRC's low-pass filtered branch.
The involvement of oscillatory shear emerged from the studies of Ku et al., who examined lesion distributions in post-mortem human arteries and used laser Doppler anemometry to measure velocity profiles in anatomically accurate models of those arteries.
In addition to simple prediction techniques, such as linear regression, we applied more robust techniques that resulted in even more accurate models.
The parameter settings resulting in the most accurate models were used to each train a separate model on the entire data set.
