Robin Ryder
@robinryder.bsky.social
850 followers 120 following 83 posts
Mathematician at Imperial College London. Bayesian statistics, Data science, Languages, Phylogenies.
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robinryder.bsky.social
Jolie illustration du paradoxe de Simpson !
Reposted by Robin Ryder
dieworkwear.bsky.social
Jane Goodall was my first childhood hero, as I loved animals as a kid and was inspired by her story. I still remember the National Geographic specials about her. RIP.
Jane Goodall reaches out and touches a small monkey.
robinryder.bsky.social
It's a great opportunity for recent PhD graduates who wish to pursue a career in academia.

Salary: in the range £49,017 - £57,472 per annum
Deadline to apply: 11 November.

Please contact me if you have questions!
robinryder.bsky.social
We are hiring!

We are inviting applications for the Chapman Fellowship in Statistics at Imperial College London.

This is a kind of super-postdoc: 3 years contract, excellent working conditions, and candidates are expected to propose an independent research plan.

www.imperial.ac.uk/jobs//search...
Imperial College London Authentication - Stale Request
www.imperial.ac.uk
Reposted by Robin Ryder
isba-bayesian.bsky.social
Proposals for contributed talks & posters are now being accepted for the 2026 ISBA World Meeting! The world meeting will take place in Nagoya, Japan between 28 June and 3 July, 2026. Proposals may be submitted here: forms.gle/dVTUrdEuVF6g...
robinryder.bsky.social
Of course! It's open to all countries.
robinryder.bsky.social
Update: the ISBA membership section has been updated. You can (and should 😉) now join our section!
Reposted by Robin Ryder
isba-bayesian.bsky.social
The newest section of ISBA has been formed to promote Bayesian methods in the social sciences! Find out more 👇
adrianraftery.bsky.social
The new Bayesian Social Sciences section of @isba-bayesian.bsky.social has just been created: bss-isba.github.io. The committee is myself as chair, @robinryder.bsky.social, chair elect from 2027, @nialfriel.bsky.social, program chair, @monjalexander.bsky.social, Treasurer, EJWagenmakers, Secretary.
Home - BSS-ISBA
bss-isba.github.io
robinryder.bsky.social
Do get in touch if you'd like to be involved.

ISBA members will be able to join the section (via the ISBA website) very soon.

The next Bayesian Methods in the Social Sciences conference is planned for December 2026 in Dublin. Watch this space!
robinryder.bsky.social
I'm delighted to serve as Chair-Elect for this new section, along with Chair @adrianraftery.bsky.social, Programme chair @nialfriel.bsky.social, Treasurer @monjalexander.bsky.social, and Secretary EJ Wagenmakers.

I'm looking forward to building this section and serving the community!
robinryder.bsky.social
I'm super excited to announce that ISBA @isba-bayesian.bsky.social has voted to start a new section on Bayesian Social Sciences! It will be a great way to further collaborations with many disciplines in the Social Sciences and Humanities.

bss-isba.github.io
Home - BSS-ISBA
bss-isba.github.io
robinryder.bsky.social
A great overview of SBI by @fxbriol.bsky.social

If you missed the conference, check out his slides!
fxbriol.bsky.social
Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group.

The slides are now available here: fxbriol.github.io/pdfs/slides-....
Reposted by Robin Ryder
spmontecarlo.bsky.social
I am pleased to announce that together with some friends, we are organising a workshop on Non-Reversible MCMC Sampling, taking place at Newcastle University from 8–10 September 2025.

Details on the programme and registration can be found at the workshop website (sites.google.com/view/probai-...).
Reposted by Robin Ryder
Reposted by Robin Ryder
New on the blog: Using Bayesian tools to be a better frequentist

Turns out that for neg. bin. regression with small samples, standard frequentist tools fail to achieve their stated goals. Bayesian computation ends up providing better frequentist guarantees. www.martinmodrak.cz/2025/07/09/u...
Using Bayesian tools to be a better frequentist
www.martinmodrak.cz
robinryder.bsky.social
The methods scale well (by ABC standards). Here is the output on real data with about 300 parameters to infer. Notice that the local parameters do vary quite a lot, and allowing for that variation allows for better inference of the global parameters.
Posterior distribution for two parameters.
Left: parameter R0. The posterior using a model at the departmental level is much more peaked that the posteriror using a model at the national level.
Right: paramer ν. There is only one curve at the national curve, but many individual posteriors at the departmental level, showing a lot of heterogeneity. Map of France showing the paramer ν_k at each department. values are lower in the North-East (ν_k of the order of 0.5 and larger in the West (values varying between 1 and 3.5).
robinryder.bsky.social
We have found that both Over Sampling and Under Matching give significant improvements with mis-specified models or outliers, as these schemes make it easier to catch the tails of the posterior predictive.
Performance of 7 algorithms. For most of the figure, the ordering is: worst ABC-SMC, the ABC-Vanilla or ABC-SMC, then permABC-Vanilla or permABC-SMC, then Under-Matching, then Over-Sampling
robinryder.bsky.social
We then progressively increase M₀<M₁<… <Mₜ=K, giving another sequential scheme. We could combine over sampling and under matching but haven't considered that in this paper.
robinryder.bsky.social
In Under Matching, we take the complementary approach: we simulate synthetic data of the appropriate size, but the distance between synthetic and observed data is only computed on the closest M₀ compartments. Again, matching is easier, since we start with the easiest parts of the data.
robinryder.bsky.social
We then progressively decrease L₀>L₁>… >Lₜ=K. This defines a different sequential scheme, which is practical because we are allowing for permutations of the data.