no code implementations • 13 Dec 2020 • Jonathan Stewart, Michael Schweinberger
We demonstrate that scalable estimation of random graph models with dependent edges is possible, by establishing convergence rates of pseudo-likelihood-based $M$-estimators for discrete undirected graphical models with exponential parameterizations and parameter vectors of increasing dimension in single-observation scenarios.
Statistics Theory Statistics Theory
