Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.
We consider how to compare different conditional independence specifications for ordinal categorical data, by calculating a posterior distribution over classes of graphical models. The approach is based on the multivariate ordinal probit model (Chib and Greenberg, 1998) where the data are considered to have arisen as truncated multivari-ate normal random… (More)
The effects of semi-annual and 3.5 yr mean ozone (O3) concentrations on children's forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) were assessed over a study period of 3.5 yrs in 2,153 schoolchildren from 15 study sites in South Western Germany and Lower Austria. Spirometric parameters were assessed twice a year, and… (More)
We propose modifications to existing Markov chain Monte Carlo algorithms to generate from the conditional distribution of an adjacency matrix, given the in-degrees, the out-degrees and the number of mutual dyads. We compare our results with those obtained by using various approximations.
A default strategy for fully Bayesian model determination for GLMMs is considered which addresses the two key issues of default prior specification and computation. In particular, the concept of unit information priors is extended to the parameters of a GLMM. A combination of MCMC and Laplace approximations is used to compute approximations to the posterior… (More)
Age and sex patterns of migration are essential for understanding drivers of population change and heterogeneity of migrant groups. We develop a hierarchical Bayesian model to estimate such patterns for international migration in the European Union and European Free Trade Association from 2002 to 2008, which was a period of time when the number of members… (More)