Bayesian Variational Inference for Exponential Random Graph Models

Bayesian inference of exponential random graph models under measurement errors

While the impact of measurement errors inherent in network data has been widely recognized, relatively little work has been done to solve the problem mainly due to the complex dependence nature of network data. In this paper, we propose a Bayesian inference framework for summary statistics of the true underlying network, based on the network observed with measurement errors. To the best of our ...

Bayesian inference of exponential random graph models for large social networks

This paper addresses the issue of sampling from the posterior distribution of exponential random graph (ERG) models and other statistical models with intractable normalizing constants. Existing methods based on exact sampling are either infeasible or require very long computing time. We propose and study a general framework of approximate MCMC sampling from these posterior distributions. We als...

Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution

Exponential random graph models are an important tool in the statistical analysis of data. However, Bayesian parameter estimation for these models is extremely challenging, since evaluation of the posterior distribution typically involves the calculation of an intractable normalizing constant. This barrier motivates the consideration of tractable approximations to the likelihood function, such ...

Exponential random graph models

Synonyms p* models, p-star models, p1 models, exponential family of random graphs, maximum entropy random networks, logit models, Markov graphs Glossary • Graph and network: the terms are used interchangeably in this essay. • Real-world network: (real network, observed network) means network data the researcher has collected and is interested in modelling. • Ensemble of graphs: means the set of...

Variational Bayesian Inference for Financial Models