Rui-Yang Zhang

The first talk of the season will be this coming Tuesday (23 September), given by Alexandre Bouchard-Côté from UBC. Alex is a great speaker, so do join if you have the chance!

See sites.google.com/view/monte-c... for details, links, and so on.

Returning soon - stay tuned!

sites.google.com/view/monte-c...

Join us online for a discussion on
“Statistical exploration of the Manifold Hypothesis” and an opportunity to explore the intersection of geometry, statistics and machine learning.

📅 Wed 08 Oct | 🕓 4–6pm UK
🔗 Register + download the paper: rss.org.uk/training-eve...

“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material.

In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵

Gearing up for this workshop next week, with the finalised schedule attached!

For those who are unable to attend in person, but are interested in watching the talks, they will be streamed live on MS Teams. Please do get in touch with me if you'd like to stay informed about the stream.

An announcement, which might be of some interest:

In the period 2022-2024, myself and a number of other postdocs on the "CoSInES" and "Bayes4Health" EPSRC grants were involved in organising a number of internal tutorial workshops, on topics relevant to researchers in computational statistics.

Very cool!

New paper on arXiv! And I think it's a good'un 😄

Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.

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-....

📣 Please share: We invite submissions to the 29th International Conference on Artificial Intelligence and Statistics (#AISTATS 2026) and welcome paper submissions at the intersection of AI, machine learning, statistics, and related areas. [1/3]

Liwen Xue, Axel Finke, Adam M. Johansen: Online Rolling Controlled Sequential Monte Carlo https://arxiv.org/abs/2508.00696 https://arxiv.org/pdf/2508.00696 https://arxiv.org/html/2508.00696

Really enjoyed listening to this interview with Mike Giles. Only knew him from his multilevel Monte Carlo work, and it was quite a nice surprise to learn about his contributions to CFD and experiences with industrial collaborations!

we're out here simulating, visualising, thriving

Congrats !!!

Gaussian Processes and Reproducing Kernels: Connections and Equivalences This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilb...

We've written a monograph on Gaussian processes and reproducing kernel methods (with @philipphennig.bsky.social, @sejdino.bsky.social and Bharath Sriperumbudur).

arxiv.org/abs/2506.17366

Line chart titled ‘Weekly Runs of RStudio IDE’ showing usage data from 2023 to 2025. The y-axis ranges from 2,000,000 to 6,000,000 weekly runs. The chart displays a cyclical pattern with regular peaks around 5,000,000-6,000,000 runs and dramatic drops to approximately 2,000,000 runs that occur periodically during holiday periods.

Is #rstats dead? I don’t think so.

Is it just me or does Google Scholar forbid searches via Avanti’s WiFi?

The talks from the Post-Bayes workshop are now available online here - youtube.com/playlist?lis... - do take a look!

In the interim, I wanted to advertise our YouTube channel - youtube.com/@montecarlos... - which contains recordings for the bulk of our talks so far (sites.google.com/view/monte-c..., sites.google.com/view/monte-c...). I encourage you to catch up and enjoy them over the intervening months!

Starting from last October, we (@OnlineMCSeminar on Twitter, sites.google.com/view/monte-c...) have been running an online seminar on all aspects of Monte Carlo methods, with about ~30 talks so far. We are currently paused for the summer, expecting to return in September 2025.

Do you happen to have anything related to low-rank approximation / matrix sketching? Thanks !!

Luke Hardcastle, Samuel Livingstone, Gianluca Baio
Diffusion piecewise exponential models for survival extrapolation using Piecewise Deterministic Monte Carlo
https://arxiv.org/abs/2505.05932

ANN: A non-reversible rejection-free HMC sampler Hej! Have you ever wondered if momentum flips/refreshments are really needed in HMC or if we somehow can avoid to lose our sense of direction after each proposal step? Or even wondered if we could ge...

Demo for the sampler from our recent paper

discourse.julialang.org/t/ann-a-non-...

Keen to read this:

arxiv.org/abs/2504.13322
'Foundations of locally-balanced Markov processes'
- Samuel Livingstone, Giorgos Vasdekis, Giacomo Zanella

Inside arXiv—the Most Transformative Platform in All of Science Modern science wouldn’t exist without the online research repository known as arXiv. Three decades in, its creator still can’t let it go.

Modern science wouldn’t exist without the online research repository known as arXiv. Three decades in, its creator still can’t let it go.

With Gibbs you can arbitrary choose the order you go through the coords, but in your MH that is not possible. The MH acceptance simplifies to p(x,y’)p(x’)p(y) / p(y,x’)p(x)p(y’). In the case where your x and y are independent (so your MH is also arbitrary in coord order), your MH is Gibbs.

Is there a ref for this?

We also had some thoughts on maybe using the discrete KSD works to extend stein thinning to discrete distributions

Would that empirical average comparison / gauging then be equivalent to computing test statistics for hypothesis (goodness of fit) tests then?