Discussion meetings

Discussion Meetings are events where articles ('papers for reading') appearing in the Journal of the Royal Statistical Society are presented and discussed. The discussion and authors' replies are then published in the relevant Journal series. About half of the meetings are organised by the Society's Research Section and the events are often preceded by an informal session on the issues raised by the papers. See our guidelines for papers for discussion.

Discussion Meetings are usually held in the early evening at the Society's premises in Errol Street, London. Any change in venue will be noted alongside the meeting details.

Preprints of journal papers are available to download to encourage discussion at our Discussion Meetings before publication in one of our journals. Other papers, such as Presidential addresses, are also available to download. All preprints available here are provisional and subject to later amendment by the authors.

Contact Judith Shorten if you would like to make a written contribution to a discussion meeting or receive a preprint for each meeting by email.

Click here to watch videos from past discussion meetings.

Preprint discussion papers

2019

Discussion Meeting, Wednesday, 13 November 2019
‘Multiple-systems analysis for the quantification of modern slavery: classical and Bayesian approaches’
Bernard W. Silverman
Pre-meeting (DeMO) at 3pm.  Presenter: Peter G M van der Heijden
An introduction to dual and multiple system estimation’
Details

Research Section Discussion Meeting, Wednesday, 11 December 2019
‘Unbiased Markov chain Monte Carlo methods with couplings’
Pierre E. Jacob, John O’Leary and Yves F. Atchadé
Pre-meeting (DeMO) at 3pm.
Presenters: Chris Sherlock and Pierre Jacob
Chair: Ioanna Manolopoulou
Details


Preprints

2019

Discussion Meeting, Wednesday, 13 November 2019

Bernard W. Silverman (University of Nottingham, UK)
‘Multiple-systems analysis for the quantification of modern slavery: classical and Bayesian approaches’

Multiple-systems estimation is a key approach for quantifying hidden populations such as the number of victims of modern slavery. The UK Government published an estimate of 10000–13000 victims, constructed by the present author, as part of the strategy leading to the Modern Slavery Act 2015. This estimate was obtained by a stepwise multiple-systems method based on six lists. Further investigation shows that a small proportion of the possible models give rather different answers, and that other model fitting approaches may choose one of these. Three data sets collected in the field of modern slavery, together with a data set about the death toll in the Kosovo conflict, are used to investigate the stability and robustness of various multiple-systems-estimate methods. The crucial aspect is the way that interactions between lists are modelled, because these can substantially affect the results. Model selection and Bayesian approaches are considered in detail, in particular to assess their stability and robustness when applied to real modern slavery data. A new Markov chain Monte Carlo Bayesian approach is developed; overall, this gives robust and stable results at least for the examples considered. The software and data sets are freely and publicly available to facilitate wider implementation and further research.

To be published in Series A; for more information go to the Wiley Online Library.

The preprint is available to download.
Multiple-systems analysis for the quantification of modern slavery: classical and Bayesian approaches’ (PDF)
Data set and computer code (ZIP)


Research Section Discussion Meeting, Wednesday, 11 December 2019
Pierre E. Jacob and John O’Leary (Harvard University, Cambridge) and Yves F. Atchadé (Boston University)
‘Unbiased Markov chain Monte Carlo methods with couplings’

Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to 1. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the estimators proposed and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high dimensional variable-selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules.

To be published in Series B; for more information go to the Wiley Online Library.

The preprint is available to download.
Unbiased Markov chain Monte Carlo methods with couplings’ (PDF)
Supporting information (PDF)