Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
Statistics Theory
2013-10-30 v3 Social and Information Networks
Statistics Theory
Abstract
Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that applies such methods to network data, and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data, by either maximum likelihood or variational estimation. The result also applies to various sub-models of the stochastic blockmodel found in the literature.
Cite
@article{arxiv.1207.0865,
title = {Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels},
author = {Peter Bickel and David Choi and Xiangyu Chang and Hai Zhang},
journal= {arXiv preprint arXiv:1207.0865},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.1214/13-AOS1124 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)