Robin Ryder

Applications are invited for Chapman Fellow in Mathematics (Statistics Section) at the Department of Mathematics.
Closing date: 11 November 2025
www.imperial.ac.uk/jobs//search...

Jolie illustration du paradoxe de Simpson !

Jane Goodall reaches out and touches a small monkey.

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.

Description Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities ment...

If the first link does not work, try this one instead:
www.imperial.ac.uk/jobs/search-...

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!

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...

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...

Of course! It's open to all countries.

Update: the ISBA membership section has been updated. You can (and should 😉) now join our section!

The newest section of ISBA has been formed to promote Bayesian methods in the social sciences! Find out more 👇

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!

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!

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

AI is helping to decode animals’ speech. Will it also let us talk with them? The complexity of vocal communication in some primates, whales and birds might approach that of human language.

Really nice coverage of our work on bonobo syntax with Simon Townsend and Martin Surbeck, by @rfieldmouse.bsky.social. Thanks so much, Rachel!

www.nature.com/articles/d41...

A great overview of SBI by @fxbriol.bsky.social

If you missed the conference, check out his slides!

Evolutionary Tempo, Supertaxa, and Living Fossils Abstract. A relationship between the rate of molecular change and diversification has long been discussed, on both theoretical and empirical grounds. Howev

Do diversification rates and molecular evolution covary? Maybe, maybe not, but it’s surprising how much falls into place if we assume they do doi.org/10.1093/sysb...

The wonderful Niamh Cahill is recruiting a PhD student (co-supervised by me!) to work on estimating and projecting population exposure to extreme sea levels. Please contact me if interested!

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-...).

Exciting new opening @nccrlanguage.bsky.social with the option of permanent extension. If you work in cutting-edge stats and are interested in joining a highly inter-disciplinary, vibrant community, don't hesitate to apply at tinyurl.com/3fxwjk8e

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...

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).

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.

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

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.

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.

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.

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.

In Over Sampling, we will instead simulate "too much" data. Say the observed data have K=94 compartments: we will initially simulate synthetic data with L₀=1000 compartments, and keep the best match for each compartment. Note that matching is easier, even for a low value of ε!

Say you are targeting a pseudo-posterior with some threshold ε. A standard ABC-SMC scheme consists in considering a (possibly adaptive) schedule ε₀>ε₁>…>εₜ=ε.
Each iteration of the SMC tightens the approximation, and eventually you reach the target pseudo-posterior with low value of ε.

We then implement two new sequential strategies:
➡️ over sampling and
➡️ under matching.

These differ from traditional ABC-SMC sequences of distributions.

Performance curve of various algorithms. For most of the figure, the ordering is: worst ABC-PMC, then ABC-Vanilla and ABC-SMC, best either permABC-Vanilla or permABC-SMC

Taken alone, this natural idea already gives us a gain of an order of magnitude.
(In this figure, lower is better.)