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

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 is… Expand

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

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

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 also… Expand

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

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

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 marginal… Expand

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 the… Expand