• Publications
  • Influence
Bayesian inference for exponential random graph models
TLDR
We show how inference can be carried out in a Bayesian framework using a MCMC algorithm, which circumvents the need to calculate the normalising constants. Expand
  • 174
  • 41
  • PDF
Marginal likelihood estimation via power posteriors
Model choice plays an increasingly important role in statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. However, this isExpand
  • 257
  • 37
  • PDF
Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
TLDR
We apply theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how ‘close’ the chain given by the transition kernel $$\hat{P}$$P^ is to the chaingiven by $$P$$P. Expand
  • 101
  • 18
  • PDF
Estimating the evidence – a review
TLDR
The model evidence is a vital quantity in Bayesian model choice and, somewhat confusingly is given different names in the literature, the marginal likelihood, integrated likelihood or evidence. Expand
  • 126
  • 11
  • PDF
Classical model selection via simulated annealing
Summary. The classical approach to statistical analysis is usually based upon finding values for model parameters that maximize the likelihood function. Model choice in this context is often alsoExpand
  • 80
  • 11
Improving power posterior estimation of statistical evidence
TLDR
The statistical evidence (or marginal likelihood) is a key quantity in Bayesian statistics, allowing one to assess the probability of the data given the model under investigation. Expand
  • 51
  • 11
  • PDF
Improved Bayesian inference for the stochastic block model with application to large networks
TLDR
An efficient MCMC algorithm is presented to cluster the nodes of a network such that nodes with similar role in the network are clustered together. Expand
  • 64
  • 8
Recursive computing and simulation-free inference for general factorizable models
We illustrate how the recursive algorithm of Reeves & Pettitt (2004) for general factorizable models can be extended to allow exact sampling, maximization of distributions and computation of marginalExpand
  • 39
  • 6
  • PDF
The effect of ball carrying method on sprint speed in rugby union football players
Different methods of ball carrying can be used when a player runs with the ball in rugby union. We examined how three methods of ball carrying influenced sprinting speed: using both hands, under theExpand
  • 33
  • 6
Block clustering with collapsed latent block models
  • J. Wyse, N. Friel
  • Computer Science, Mathematics
  • Stat. Comput.
  • 12 November 2010
TLDR
We introduce a Bayesian extension of the latent block model for model-based block clustering of data matrices. Expand
  • 55
  • 5
  • PDF