Archive for ABC

OWABI⁷, 29 January 2026: Sequential Neural Score Estimation (11am UK time)

Posted in Books, Statistics, University life with tags , , , , , , , , , , on January 21, 2026 by xi'an

Speaker: Louis Sharrock (University College London)

Title: Sequential Neural Score Estimation: Likelihood-free inference with conditional score base diffusion models
Abstract: We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable success of score-based methods in generative modelling, leverages conditional score-based diffusion models to generate samples from the posterior distribution of interest. The model is trained using an objective function which directly estimates the score of the posterior. We embed the model into a sequential training procedure, which guides simulations using the current approximation of the posterior at the observation of interest, thereby reducing the simulation cost. We also introduce several alternative sequential approaches, and discuss their relative merits. We then validate our method, as well as its amortised, non-sequential, variant on several numerical examples, demonstrating comparable or superior performance to existing state-of-the-art methods such as Sequential Neural Posterior Estimation (SNPE).
Keywords: diffusion models, simulation based inference, sequential methods.
Reference: L. Sharrock, J. Simons, S. Liu, M. Beaumont, Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models. PLMR, 235, 44565-44602, 2024.

Karim Benabed, astrophysician

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , on December 5, 2025 by xi'an

The astronomer and cosmologist Karim Benabed got killed on Wednesday in Paris. While cycling, run over by a truck-driver (with no further details at the moment). He was a senior research at IAP (Institut d’Astrophysique de Paris) and we actively collaborated together between 2005 and 2010 on an ANR project on efficient simulation methods for inferring cosmological parameters, based on PMC. And Bayesian model comparison. He was a very congenial person, very sharp in assimilating new methods and keen on exploring novel hypotheses. While we did not keep closely in touch, I would meet him now and then while visiting the IAP. Ironically, Darren Wraith, formerly a postdoc with him at IAP,  was visiting me last week and we were reminiscing of that era as late as Saturday night over dinner… So sad (and also so absurd, a truck stopping the trajectory of someone managing to travel to the origins of the Universe). The above is a cartoon of him drawn during his cosmic microwave background presentation during the Nuit de l’Astronomie.

to the early Universe and back

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , on November 16, 2025 by xi'an

On 28 October, I spent the day at Institut d’Astrophysique de Paris (where I used to work on PMC for cosmology between 2005 and 2009), as a committee member for the habilitation defence of Florent Leclercq. Not only it was nice to be back in this unique institution (with vestiges from Laplace’s era), but this was a fantastic habilitation, with a superb thesis that beautifully gathered the different fields mastered by the candidate in a highly coherent discourse. And could serve as an introduction to cosmostatistics for many.

And provided the background to ten years (post-PhD) of research on forward modelling in cosmology and resulting Bayesian statistical analysis either by implicit likelihood (or likelihood-free) inference or by field-level inference. He describes the Simbelmynë software he developed to produce maps of the density field and analyse dark matter dynamics. Ẁhose name is borrowed from Tolkien (along with a quote from Guy Gavriel Kay!):

“How fair are the bright eyes in the grass! Evermind they are called, simbelmynë in this land of Men, for they blossom in all the seasons of the year, and grow where dead men rest.” — J.R.R. Tolkien, The Lord of the Ring

And the Bayesian computational and modelling tools he elaborated, like SELFI (Simulator expansion for likelihood-free inference, Leclercq et al., 2019), that relates to Michael Gutmann’s and Juka Corander’s BOLFI. (Obviously, I did not get every aspect right from just reading the thesis and attending the lecture, in particular the remarks on using SELFI to assess model misspecification. But I remain impressed by the scope of the work and its likely impact on the field!)

Scottish June

Posted in Mountains, pictures, University life with tags , , , , , , , , , , , , , , , on October 23, 2025 by xi'an

I just found out that three almost consecutive events of academic interest are taking place in Scotland next Spring: first, our very own Approximately Bayes ICMS workshop (on the Isle of Skye, rather than at the Bayes Centre in Edinburgh, where we held ABC in Edinburgh), on 17-22 May

the SIAM Conference on Optimization (OP26) in Edinburgh on 2-5 June

and the Monte Carlo + quasi Monte Carlo (MCqMC 2026) conference in Edinburgh on 8-11 June

While I cannot realistically (!) attend all of these events, this accumulation of meetings is a perfect opportunity to enjoy the Athens of the North (aka Dùn Èideann, from which Dunnedin in New Zealand originates!). And the surrounding mountains.

OWABI Season VII

Posted in Statistics with tags , , , , , , , , , , , , , on October 17, 2025 by xi'an

A new season of the One World Approximate Bayesian Inference (OWABI) Seminar is about to start!
The 1st OWABI talk of the Season will be given by François-Xavier Briol (University College London). who will talk about “Multilevel neural simulation-based inferenceon Thursday the 30th October at 11am UK time.
Abstract
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences and engineering, where writing down a likelihood can be significantly more challenging than constructing a simulator. However, the performance of neural SBI can suffer when simulators are computationally expensive, thereby limiting the number of simulations that can be performed. In this paper, we propose a novel approach to neural SBI which leverages multilevel Monte Carlo techniques for settings where several simulators of varying cost and fidelity are available. We demonstrate through both theoretical analysis and extensive experiments that our method can significantly enhance the accuracy of SBI methods given a fixed computational budget.
Keywords: Multifidelity, neural SBI, multi-level Monte Carlomultilevel Monte Carlo