Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MSP.2012.2235191 | IEEE Signal Process. Mag. |
Keywords | Field | DocType |
Bayes methods,Markov processes,Monte Carlo methods,complex networks,telecommunication networks,Markov chain processing,Monte Carlo method,complex network,finite parametric model,infinite mixture model,nonparametric Bayesian modeling | Parametric model,Markov chain Monte Carlo,Computer science,Nonparametric bayesian,Nonparametric statistics,Complex network,Artificial intelligence,Machine learning,Mixture model,Model complexity,Bayesian nonparametrics | Journal |
Volume | Issue | ISSN |
30 | 3 | Signal Processing Magazine, IEEE (Volume:30, Issue:3, Year:2013) |
Citations | PageRank | References |
5 | 0.60 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikkel N. Schmidt | 1 | 277 | 26.13 |
| Morten Mørup | 2 | 704 | 51.29 |