Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models
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Publication:1658490
DOI10.1016/j.csda.2016.10.015zbMath1466.62119OpenAlexW2543295464WikidataQ115577951 ScholiaQ115577951MaRDI QIDQ1658490
Publication date: 14 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1028&context=niasrawp
Related Items (9)
Large-scale estimation of random graph models with local dependence ⋮ Bayesian duality and risk analysis on the statistical manifold of exponential family with censored data ⋮ Finite mixtures of ERGMs for modeling ensembles of networks ⋮ Approximate Bayesian computation using asymptotically normal point estimates ⋮ Rejoinder to Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” ⋮ Exponential-family random graph models for multi-layer networks ⋮ Exponential-family models of random graphs: inference in finite, super and infinite population scenarios ⋮ Convergence of contrastive divergence algorithm in exponential family ⋮ Local Graph Stability in Exponential Family Random Graph Models
Uses Software
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