Thermodynamic stability assessment of nanocrystal systems requires precise free energy calculations. This study highlights the importance of meticulous control over various factors, including the thermostat, time step, potential cutoff, initial configuration, sampling method, and overall simulation duration. Free energy computations in dry (solvent-free) systems are on the order of several hundred kBT but can be obtained with consistent accuracy. However, calculation of internal energies becomes challenging, as they are typically much larger in magnitude than free energies and exhibit significant noise and reduced reliability. To address this limitation, we propose a new internal energy estimate that drastically reduces the noise. We also present formulas that enable the optimization of the parameters of the harmonic bias potential for optimal convergence. Finally, we discuss the implications of these findings for the computation of free energies in nanocrystal clusters and superlattices.

MultiBUGS is a new version of the general-purpose Bayesian modeling software BUGS that implements a generic algorithm for parallelizing Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelizes evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelizes sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelize the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational cores.

We propose a simple, generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms for posterior inference of Bayesian hierarchical models. Our algorithm parallelises evaluation of the product-form likelihoods formed when a parameter has many children in the hierarchical model; and parallelises sampling of conditionally-independent sets of parameters. A simple heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. The algorithm enables automatic parallelisation of the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate our approach using our freely-available open source implementation, called MultiBUGS (this https URL), on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Reliable posterior inference for this model takes several hours in existing software, but MultiBUGS can perform inference in only 29 minutes using 48 computational cores.

Statistics in MedicineVolume 28, Issue 25 p. 3081-3082 Rejoinder Rejoinder to commentaries on 'The BUGS project: Evolution, critique and future directions' David Lunn, David Lunn [email protected] MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K.Search for more papers by this authorDavid Spiegelhalter, David Spiegelhalter [email protected] Institute of Public Health, University of Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K.Search for more papers by this authorAndrew Thomas, Andrew Thomas [email protected] School of Mathematics and Statistics, University of St Andrews, St Andrews, U.K.Search for more papers by this authorNicky Best, Nicky Best [email protected] Division of Epidemiology, Public Health and Primary Care Imperial College, South Kensington Campus, London SW7 2AZ, U.K.Search for more papers by this author David Lunn, David Lunn [email protected] MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K.Search for more papers by this authorDavid Spiegelhalter, David Spiegelhalter [email protected] Institute of Public Health, University of Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K.Search for more papers by this authorAndrew Thomas, Andrew Thomas [email protected] School of Mathematics and Statistics, University of St Andrews, St Andrews, U.K.Search for more papers by this authorNicky Best, Nicky Best [email protected] Division of Epidemiology, Public Health and Primary Care Imperial College, South Kensington Campus, London SW7 2AZ, U.K.Search for more papers by this author First published: 13 October 2009 https://doi.org/10.1002/sim.3691Citations: 9AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES 1 Girgis IG, Schaible T, Nandy P, De Ridder F, Mathers J, Mohanty S. Parallel Bayesian methodology for population analysis. 2007. Available at: www.page-meeting.org/?abstract=1105. 2 Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006; 1: 515–533. 3 Lunn D, Best N, Spiegelhalter D, Graham G, Neuenschwander B. Combining MCMC with 'sequential' PKPD modelling. Journal of Pharmacokinetics and Pharmacodynamics 2009; 36: 19–38. 4 Lunn DJ, Best N, Whittaker JC. Generic reversible jump MCMC using graphical models. Statistics and Computing 2008; DOI: 10.1007/s11222-008-9100-0. Citing Literature Volume28, Issue2510 November 2009Pages 3081-3082 ReferencesRelatedInformation

BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20‐year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives. Copyright © 2009 John Wiley & Sons, Ltd.

3-Dimensional concurrent engineering has been put forward as a way of achieving step improvements in product development and delivery processes. However, the effective application of 3-dimensional concurrent engineering requires tools and techniques that will support practitioners who wish to use it. This paper reports research that has explored the information requirements that need to be satisfied by tools and techniques intended to support the execution of 3-dimensional concurrent engineering processes. A case study showing the extent, depth and richness of the information and associated structures that is required for effective 3-dimensional concurrent engineering is presented.

Markov chain Monte Carlo (MCMC) techniques have revolutionized the field of Bayesian statistics by enabling posterior inference for arbitrarily complex models. The now widely used WinBUGS software has, over the years, made the methodology accessible to a great many applied scientists, in all fields of research. Despite this, serious application of MCMC methods within the field of population PK/PD has been comparatively limited. We appreciate that for many applied pharmacokineticists the prospect of conducting a Bayesian analysis will require numerous alien concepts to be taken on board and it may be difficult to justify investing the time and effort required in order to understand them (especially since the approach is so computer-intensive). For this reason we provide here a thorough (but often informal) discussion of all aspects of Bayesian inference as they apply specifically to population PK/PD. We also acknowledge that while the WinBUGS software is general purpose, model specification for some types of problem, population PK/PD being a prime example, can be very difficult, to the extent that a specialized interface for describing the problem at hand is often a practical necessity. In the latter part of this paper we describe such an interface, namely PKBugs. A principal aim of the paper is to offer sufficient technical background, in an easy to follow format, that the reader may develop both the confidence and know-how to make appropriate use of the PKBugs/WinBUGS framework (or similar software) for their own data analysis needs, should they choose to adopt a Bayesian approach.

WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an object-oriented representation of the model. The software offers a user-interface, based on dialogue boxes and menu commands, through which the model may then be analysed using Markov chain Monte Carlo techniques. In this paper we discuss how and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design. We also discuss how the framework may be extended. It is possible to write specific applications that form an apparently seamless interface with WinBUGS for users with specialized requirements. It is also possible to interface with WinBUGS at a lower level by incorporating new object types that may be used by WinBUGS without knowledge of the modules in which they are implemented. Neither of these types of extension require access to, or even recompilation of, the WinBUGS source-code.

This Addendum speciies additional features of BUGS 0.6, and should beread in conjunction with the current manual for BUGS 0.5 Spiegelhalter et al., 1996a .

Contents1 Getting started 21.1 Getting the software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 The script file for `bugs" (Sparc) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 The script file for `backbugs" (Sparc) . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 New Facilities in 0.6 32.1 Checkpoint command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Metropolis sampling . . . . . . . . . . . . . ...

Models with complex structure arise in many social science applications and appear natural candidates for the use of Markov chain Monte Carlo methods for inference. Conditional independence assumptions simplify the model specification and make estimation using Gibbs sampling particularly appropriate. Two examples are discussed: random effects models for repeated ordered categorical data and sensitivity analysis to assumptions concerning the mechanism underlying informative drop-out in a longitudinal study. The use of a program BUGS is demonstrated.

e wrong, which is even worse. Please let us know of anysuccesses or failures.Beware - Gibbs sampling can be dangerous!.BUGS c flcopyright MRC Biostatistics Unit 1995. ALL RIGHTS RESERVED. The support of the Economic andSocial Research Council (UK) is gratefully acknowledged. The work was funded in part by ESRC (UK) AwardNumber H519 25 5023.12Contents1 Introduction 51.1 What is BUGS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2 For what kind ...