43 citations · 242 across the 46 of their papers we have counts for
26 papers · 1 filter
Statistical Network Analysis with Bergm
Alberto Caimo, Lampros Bouranis, Robert Krause +1
Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerge…
Bayesian variational inference for exponential random graph models
Linda S. L. Tan, Nial Friel
Deriving Bayesian inference for exponential random graph models (ERGMs) is a challenging "doubly intractable" problem as the normalizing constants of the likelihood and posterior d…
Computationally efficient inference for latent position network models
Riccardo Rastelli, Florian Maire, Nial Friel
Latent position models are widely used for the analysis of networks in a variety of research fields. In fact, these models possess a number of desirable theoretical properties, and…
Efficient MCMC for Gibbs Random Fields using pre-computation
Aidan Boland, Nial Friel, Florian Maire
Bayesian inference of Gibbs random fields (GRFs) is often referred to as a doubly intractable problem, since the likelihood function is intractable. The exploration of the posterio…
Bayesian inference, model selection and likelihood estimation using fast rejection sampling: the Conway-Maxwell-Poisson distribution
Alan Benson, Nial Friel
Bayesian inference for models with intractable likelihood functions represents a challenging suite of problems in modern statistics. In this work we analyse the Conway-Maxwell-Pois…
Noisy Hamiltonian Monte Carlo for doubly-intractable distributions
Julien Stoehr, Alan Benson, Nial Friel
Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician's toolbox as an alternative sampling method in settings when standard Metropolis-Hastings…