43 citations · 242 across the 46 of their papers we have counts for
5 papers · 1 filter
Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution
Lampros Bouranis, Nial Friel, Florian Maire
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 e…
Properties of Latent Variable Network Models
Riccardo Rastelli, Nial Friel, Adrian E. Raftery
We derive properties of Latent Variable Models for networks, a broad class of models that includes the widely-used Latent Position Models. These include the average degree distribu…
Light and Widely Applicable MCMC: Approximate Bayesian Inference for Large Datasets
Florian Maire, Nial Friel, Pierre Alquier
Light and Widely Applicable (LWA-) MCMC is a novel approximation of the Metropolis-Hastings kernel targeting a posterior distribution defined on a large number of observations. Ins…
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Julien Stoehr, Nial Friel
Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoo…
Investigation of the widely applicable Bayesian information criterion
N. Friel, J. P. McKeone, C. J. Oates +1
The widely applicable Bayesian information criterion (WBIC) is a simple and fast approximation to the model evidence that has received little practical consideration. WBIC uses the…