Dynamic Cone Penetration Test (CPTu) profile offshore sediments by impact penetration. To exploit their results in full, the measured data are converted to obtain a quasi-static equivalent profile. CPTu conversion requires calibrated correction models. Calibration is currently done using paired (i.e., very close) and dynamic tests. It shown here that test data, which may be inconvenient acquire...

We consider a multivariate lognormal frailty model for correlated exchangeable failure time data, where the marginal lifetimes have conditional Weibull distributions. We discuss Bayesian statistical methods to fit this model to experimental data with varying cluster sizes. The Bayesian inferential approach arises naturally from the hierarchical structure of the frailty model. In contrast, imple...

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an “optimal” number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results...

In this paper we discuss ways to use Bayesian methodology to estimate the matching performance of a biometric identification device when no errors are detected. One of the drawbacks to the classical or frequentist statistical estimation methods is that it is not possible to create a confidence interval for the error rate when no errors are observed. In this paper we begin by discussing the rele...

A Bayesian methodology has been developed for multiple quantitative trait loci (QTL) mapping of complex binary traits that follow liability threshold models. Unlike most QTL mapping methods where only one or a few markers are used at a time, the proposed method utilizes all markers across the genome simultaneously. The outperformance of our Bayesian method over the traditional single-marker ana...

A general Gibbs sampling algorithm for analyzing a broad class of linear models under a Bayesian framework is presented using Markov Chain Monte Carlo (MCMC) methodology in the SAS system. The analysis of a North Central Cancer Treatment Group (NCCTG) oncology clinical trial involving a two-period two-treatment crossover design is presented as an example. Results for the Bayesian model are comp...

I warmly congratulate the authors on this paper. I am sure they will succeed in broadening acceptance of the Bayesian paradigm in inference in regression, by providing this well-written and accessible treatment of the use of Markov chain Monte Carlo (MCMC) in tting the important class of (generalised) additive models. The paper promotes several ideas. It can be interesting and revealing to exam...