Search Results for author:
Found 3 papers, 1 papers with code
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
We consider a non-projective class of inhomogeneous random graph models with interpretable parameters and a number of interesting asymptotic properties.
Bayesian inference on random simple graphs with power law degree distributions
The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption.
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models
Bayesian nonparametric methods based on the Dirichlet process (DP), gamma process and beta process, have proven effective in capturing aspects of various datasets arising in machine learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
