43 citations · 242 across the 46 of their papers we have counts for
5 papers · 1 filter
Choosing the number of clusters in a finite mixture model using an exact Integrated Completed Likelihood criterion
Marco Bertoletti, Nial Friel, Riccardo Rastelli
The integrated completed likelihood (ICL) criterion has proven to be a very popular approach in model-based clustering through automatically choosing the number of clusters in a mi…
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods
Nial Friel, Antonietta Mira, Chris. J. Oates
Many popular statistical models for complex phenomena are intractable, in the sense that the likelihood function cannot easily be evaluated. Bayesian estimation in this setting rem…
Bayesian Exponential Random Graph Models with Nodal Random Effects
Stephanie Thiemichen, Nial Friel, Alberto Caimo +1
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Baye…
Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels
P. Alquier, N. Friel, R. Everitt +1
Monte Carlo algorithms often aim to draw from a distribution by simulating a Markov chain with transition kernel such that is invariant under . However, there are ma…
Inferring structure in bipartite networks using the latent block model and exact ICL
Jason Wyse, Nial Friel, Pierre Latouche
We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelih…