We consider the problem of mapping the risk from a disease using a series of regional counts of observed and expected cases. To analyse this problem from a Bayesian viewpoint we propose a methodology, which extends Markov random elds priors by including a partially exchangeable set of parameters. Such an extension allows detection of clusters between remote regions, reeecting some underlying ca...

To support building and maintaining knowledge-based systems for real-life application domains, sophisticated knowledgeengineering methodologies are available. As more and more Bayesian networks are being developed for complex applications, their construction and maintenance calls for the use of tailor-made knowledge-engineering methodologies. We have designed such a methodology and have studied...

In this paper we develop a Bayesian statistical inference approach to the unified analysis of isobaric labelled MS/MS proteomic data across multiple experiments. An explicit probabilistic model of the log-intensity of the isobaric labels' reporter ions across multiple pre-defined groups and experiments is developed. This is then used to develop a full Bayesian statistical methodology for the id...

Multivariate quantiles have been defined by a number of researchers and can be estimated by different methods. However, little work can be found in the literature about Bayesian estimation of joint quantiles of multivariate random variables. In this paper we present a multivariate quantile function model and propose a Bayesian method to estimate the model parameters. The methodology developed h...

In this paper, we propose a methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make para...

In Neal (2010), a novel Approximate Bayesian Computation (ABC) algorithm, coupled ABC, was introduced. This paper shows how coupled ABC can be used in an efficient manner for model choice in a Bayesian framework. The methodology is applied to Gibbs random fields and stochastic epidemic models. Furthermore a very efficient simulation procedure for Gibbs random fields with a given sufficient summ...

Understanding the evolution of the Universe from the Big Bang to the current day is the fundamental goal of cosmology. A major part of this is the problem of structure formation: understanding the formation, growth and subsequent evolution of galaxies in the presence of Dark Matter. The world leading Galform group, based at the Institute of Computational Cosmology, Durham University, has develo...

• Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8].

The essential role of the likelihood function in both Bayesian and non-Bayesian inference is described. Several topics related to the extension of likelihood-based methodology to more complex settings are reviewed, including modifications to profile likelihood, composite and pseudo-likelihoods, quasi-likelihood, semiparametric and non-parametric likelihoods, and empirical likelihood .  2010 Jo...

First, there is the authors’ recognition that methodology is ineluctably bound up with philosophy. If nothing else “strictures derived from philosophy can inhibit research progress” (p. 4). They note, for example, the reluctance of some Bayesians to test their models because of their belief that “Bayesian models were by definition subjective,” or perhaps because checking involves non-Bayesian m...