Journal article · Preprint article
Nonparametric Bayesian Modeling of Complex Networks: An Introduction
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric 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 nonparametric models for complex networks can be derived and point out relevant literature.
Language: | English |
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Publisher: | IEEE |
Year: | 2013 |
Pages: | 110-128 |
ISSN: | 15580792 and 10535888 |
Types: | Journal article and Preprint article |
DOI: | 10.1109/MSP.2012.2235191 |
ORCIDs: | Schmidt, Mikkel Nørgaard and Mørup, Morten |
Adaptation models Bayes methods Complex networks Learning systems Markov chain processing Markov processes Modeling Monte Carlo method Monte Carlo methods complex network complex networks finite parametric model infinite mixture model nonparametric Bayesian modeling stat.ML telecommunication networks