Non-parametric Bayesian modeling of complex networks
Machine Learning
2013-12-23 v1
Abstract
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature.
Cite
@article{arxiv.1312.5889,
title = {Non-parametric Bayesian modeling of complex networks},
author = {Mikkel N. Schmidt and Morten Mørup},
journal= {arXiv preprint arXiv:1312.5889},
year = {2013}
}