A Stein goodness-of-test for exponential random graph models
We propose and analyse a novel nonparametric goodness-of-fit testing procedure for exchangeable exponential random graph model (ERGM) when a single network realisation is observed. The test determines how likely it is that the observation is generated from a target unnormalised ERGM density. Our tes...
| Hauptverfasser: | , |
|---|---|
| Format: | Journal article |
| Sprache: | English |
| Veröffentlicht: |
Journal of Machine Learning Research
2021
|