Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models
type: Item
instance of: scholarly article
title: Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models
zbMATH Open document ID: 1466.62119
author: Pavel N. Krivitsky
published in: Computational Statistics and Data Analysis
publication date: 2018-08-14T00:00:00Z
full work available at URL: https://ro.uow.edu.au/cgi/viewcontent.cgi
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 6917854
zbMATH Keywords: curved exponential family
zbMATH Keywords: ERGM
zbMATH Keywords: network data
zbMATH Keywords: partial stepping
describes a project that uses: MPNet
describes a project that uses: R
describes a project that uses: ergm
describes a project that uses: statnet
describes a project that uses: PNet
MaRDI profile type: Publication
OpenAlex ID: W2543295464
Wikidata QID: Q115577951
cites work: Q4092750
cites work: Q4023085
cites work: Modeling social networks from sampled data
cites work: Training Products of Experts by Minimizing Contrastive Divergence
cites work: Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines
cites work: Exponential-family random graph models for valued networks
cites work: Q3825957
cites work: Long range search for maximum likelihood in exponential families
cites work: Q4365843
cites work: On the geometry of discrete exponential families with application to exponential random graph models
cites work: A Stochastic Approximation Method
cites work: Logit models and logistic regressions for social networks. III: Valued relations
cites work: A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution
cites work: Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
DOI: 10.1016/J.CSDA.2016.10.015
description: scientific article; zbMATH DE number 6917854