e wrong, which is even worse. Please let us know of any successes or failures. Beware - Gibbs sampling can be dangerous!. BUGS c flcopyright MRC Biostatistics Unit 1995. ALL RIGHTS RESERVED. The support of the Economic and Social Research Council (UK) is gratefully acknowledged. The work was funded in part by ESRC (UK) Award Number H519 25 5023. 1 2 Contents 1 Introduction 5 1.1 What is BUGS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 For what kind of problems is BUGS best suited? . . . . . . . . . . . . . . . . . . . . . 5 1.3 Markov Chain Monte Carlo (MCMC) techniques . . . . . . . . . . . . . . . . . . . . 5 1.4 A simple example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Hardware platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Software . . .

We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. We follow Dempster in examining the posterior distribution of the log-likelihood under each model, from which we derive measures of fit and complexity (the effective number of parameters). These may be combined into a Deviance Information Criterion (DIC), which is shown to have an approximate decision-theoretic justification. Analytic and asymptotic identities reveal the measure of complexity to be a generalisation of a wide range of previous suggestions, with particular reference to the neural network literature. The contributions of individual observations to fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. The procedure is illustrated in a number of examples, and throughout it is emphasised that the required quantities are trivial to compute in a Markov chain Monte Carlo analysis, and require no analytic work for new...

There has been a growing interest over recent years in the use of performance indicators in healthcare, which may measure aspects of the process of care, clinical outcomes or the incidence of disease (NHS Executive, 1995; Scottish Office, 1995; New York State Department of Health, 1996). In response a sizeable literature has emerged questioning the very use of such indicators as a measure of 'quality of care', as well as stating more specific criticisms of the statistical methods used to obtain estimates adjusted for patient case-mix (DuBois et al., 1987; Jencks et al., 1988; Epstein, 1995; Schneider and Epstein, 1996). We do not attempt to further this general discussion of performance indicators and risk adjustment - see, for example (Goldstein and Spiegelhalter, 1996). Rather, the purpose of this chapter is to highlight how recent developments in computer-intensive methods can be used to explore a wide range of plausible statisti

This paper presents several models for investigating whether the HLA allogenotypes DR1/Br, DR3 and DR10 are genetic markers for a predisposition of experiencing unexplained recurrent fetal losses. A total of 199 women from 113 families answered questionnaires concerning their pregnancies and 145 of these women were HLA typed. The analysis of the data is complicated as dependencies between pregnancy outcomes are expected.

and produce answers that are wrong, which is even worse. Please let us know of any successes or failures. Beware - Gibbs sampling can be dangerous!. BUGS c flcopyright MRC Biostatistics Unit 1995. ALL RIGHTS RESERVED. The support of the Economic and Social Research Council (UK) is gratefully acknowledged. The work was funded in part by ESRC (UK) Award Number H519 25 5023. 1 2 Contents 1 Introduction 5 1.1 What is BUGS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 For what kind of problems is BUGS best suited? . . . . . . . . . . . . . . . . . . . . . 5 1.3 Markov Chain Monte Carlo (MCMC) techniques . . . . . . . . . . . . . . . . . . . . 5 1.4 A simple example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Hardware platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

We develop a statistical framework for the quantitative characterization and analysis of force-extension curves obtained from single-molecule force spectroscopy (SMFS) measurements. We apply this methodology to force-extension data obtained for elastinlike polypeptides (ELPs) with precisely engineered molecular architectures, where we demonstrate that our approach enables SMFS to be used to study hydrophobic hydration in intrinsically unstructured biomacromolecules. Our results obtained for ELPs suggest that hydrophobic hydration, rather than local backbone conformational entropy, is the key contributor to modulating the molecular elasticity of ELPs under changes in amino acid sequence. As with previous analysis, we parametrize SMFS curves using models from polymer statistical mechanics; however, we introduce several statistical innovations that dramatically improve the precision of the estimated parameters. Our approach (i) accounts for increased thermal noise in the data at low forces, (ii) provides confidence intervals for fitted polymer-theory parameters obtained from nonparametric bootstrapping,

This paper considers experiences of teaching Bayesian statistical methods within a bio-medical research setting to both statisticians and non-statisticians at postgraduate level. In particular, it considers topics covered, level of mathematical exposition, software and texts.

We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially correlated continuous response data are widespread, but models for spatially correlated categorical data, and especially ordered multicategory data, are less developed. Bayesian models and methodology have been proposed for the analysis of independent and clustered ordered categorical data, and also for binary and count point-referenced spatial data. We combine and extend these methods to describe a Bayesian model for point-referenced (as opposed to lattice) spatially correlated ordered categorical data. We include extensive simulation results and show that our model offers superior predictive performance as compared to a non-spatial cumulative probit model and a more standard generalized linear model with spatial random effects. We demonstrate the usefulness of our model using a real-world example to predict ordered categories describing stream health within the state of Maryland. Key words: Bayesian, ordinal, benthic IBI, generalized linear mixed models