Sentence examples for a stochastic block from inspiring English sources

The model is termed a stochastic block model, which in our context means that we assign proteins to blocks (or groups), and the probability of an interaction pattern between two proteins depends only on the groups to which they are assigned.

A stochastic block model M is a generative model for a network G.

This score is additive, and summing over all ϕ scores from the bottom clusters (individual vertices) upwards is equivalent to the log-likelihood ratio for the model with collapsed versus uncollapsed fine structure, with the collapsed vertices being the top-level groups in a stochastic block model.

This work, in contrast, uses a rigorous probabilistic framework to translate a hierarchical stochastic block model to the dynamic domain.

The HAC method maximizes the likelihood of a hierarchical stochastic block model, also known as the likelihood modularity (Bickel and Chen, 2009).

These data sets are joined in a probabilistic model, termed a dynamic hierarchical stochastic block model, to infer network evolution.

Here we introduce an approach that groups proteins according to shared interaction patterns through a dynamical hierarchical stochastic block model.

(A ) The infinite stochastic block model (which only uses connectivity information) over-estimates the number of classes as it fails to take distance into account, whereas our modeling of the combination of distance and connectivity finds close to the true number of classes.

First, we developed a new mathematical model, the Stochastic Block Model with Path Selection (SBM-PS) that simulates biological network formation based on the selection of edges that increase clustering.

The goal is to infer a corresponding sequence of time-evolving stochastic block models, { M t ) : t=1,…, T}, where each M t ) is a good network−generative model for G(t ).

Miklos Racz: (1) A primer on exact recovery in the general stochastic block model; (2) Estimating the dimension of a random geometric graph on a high-dimensional sphere; (3) The fundamental limits of dimension estimation in random geometric graphs; (4) Entropic central limit theorems and a proof of the fundamental limits of dimension estimation.