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a) Probabilistic methods In probabilistic methods, the random variables are modeled by probability density functions, and applicable methods include the chance-constrained programming method and the two-stage stochastic programming method [42].
We evaluate the performance of the TBAG prediction method for different covariates and different sampling methods: the random and the stratified sampling, all of which were explained in Section 3.4.
Second, in contrast to other approaches like short-path-based methods, the random walk framework incorporates the network structure around a single or multiple points of interests extensively, taking into account not only the closeness of targets, but also the multitude of the paths to the targets.
The PAC-Bayesian analysis is an abbreviation for the Probably Approximately Correct learning model and has been introduced a decade ago (Shawe-Taylor and Williamson [1]; Shawe-Taylor et al. [2]) and has made a significant contribution to the analysis and development of supervised learning methods, the random multiarmed bandits problem and so on.
For comparison, we use two baseline methods: the random selection baseline and the TF IDF weight baseline.
These results suggest that, of the different methods, the random stimuli paradigm may be more likely to systematically change over time.
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Comparing to the criteria-based methods, the random-walk-based methods are capable of extracting local information from a big graph without the knowledge of the whole graph data.
In the proposed method, the random properties of tightening torque, bolt-hole clearance, lamina properties and geometric parameters were involved.
Another machine learning method, the random forest (RF), is detailed in online supplementary materials.
The test statistic of our second method, the random set method (RS), weighs each gene by its p-value to determine enrichment.
In our method, the random walk algorithm shown in Algorithm 1 is used to rank the relations according to the random access probability.
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