Discussion meetings are events where articles ('papers for reading') appearing in the Journal of the RSS are presented and discussed. The discussion and authors' replies are then published in the relevant Journal series.
Read more about our discussion meetings, including guidelines for papers for discussion.
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.
Past Discussion meetings
Testing by betting: A strategy for statistical and scientific communication
Wednesday 9 September 2020, 4.15-6.15pm (BST)
Discussion paper ‘Testing by betting: A strategy for statistical and scientific communication’ will be presented by the author, Glenn Shafer, Rutgers University, USA at our conference, which this year is taking place online.
The preprint for the paper is available below and we welcome your contributions in the usual way during the meeting and/or in writing afterwards by 23 September 2020.
To be published in Series A; for more information go to the Wiley Online Library.
Register for the event or download the preprint (PDF) and the presentation slides (PDF).
Find the discussants slides below (PDF).
Philip Dawid (proposer)
Quasi-stationary Monte Carlo methods and the ScaLE algorithm
Wednesday 24 June, 5-7pm (rescheduled from April 2020), online
DeMO pre-meeting, 3:30-4:30pm with Paul Fearnhead and Murray Pollock
The Discussion paper 'Quasi-stationary Monte Carlo methods and the ScaLE algorithm' was presented by Murray Pollock, Paul Fearnhead, Adam M Johansen and Gareth O Roberts in this online interactive discussion meeting.
Download the preprint (PDF)
Linear mixed effects models for non-Gaussian continuous repeated measurement data
Wednesday, 13 May 2020 at 4pm, online
The Discussion paper ‘Linear mixed effects models for non-Gaussian continuous repeated measurement data’ was presented by the authors, Ozgur Asar, David Bolin, Peter J Diggle and Jonas Wallin.
To be published in Series C; for more information go to the Wiley Online Library.