Sampling random walks is a crucial component of many graph algorithms that perform graph embedding, link prediction, and other tasks. The effectiveness of these stochastic algorithms coupled with the ...

In this article, we consider estimation of parameters of random effects models from samples collected via complex multistage designs. Incorporation of sampling weights is one way to reduce estimation ...

Similarly, the novel Weighted Jump Random Walk approach has addressed the common challenge of repeated sampling—enhancing both accuracy and computational efficiency [2].

Moreover, a novel FWRF architecture is developed, replacing simple random sampling in random forest (RF) with weighted random sampling for feature subset formation. The assignment of weights to each ...

We also discuss collapsed Gibbs sampling, Pólya urn Gibbs sampling and a Pólya urn SIS scheme. Our framework allows for numerous applications, including multiplicative counting process models subject ...