Archive for Bayesian inference

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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.

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Approximate Bayesian Computation with Statistical Distances for Model Selection [OWABI, 27 Nov]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , on November 17, 2025 by xi'an

The next OWABI seminar is delivered by Clara Grazian (University of Sidney), who will talk about “Approximate Bayesian Computation with Statistical Distances for Model Selection” on Thursday 27 November at 11am UK time:

Abstract: Model selection is a key task in statistics, playing a critical role across various scientific disciplines. While no model can fully capture the complexities of a real-world data-generating process, identifying the model that best approximates it can provide valuable insights. Bayesian statistics offers a flexible framework for model selection by updating prior beliefs as new data becomes available, allowing for ongoing refinement of candidate models. This is typically achieved by calculating posterior probabilities, which quantify the support for each model given the observed data. However, in cases where likelihood functions are intractable, exact computation of these posterior probabilities becomes infeasible. Approximate Bayesian computation (ABC) has emerged as a likelihood-free method and it is traditionally used with summary statistics to reduce data dimensionality, however this often results in information loss difficult to quantify, particularly in model selection contexts. Recent advancements propose the use of full data approaches based on statistical distances, offering a promising alternative that bypasses the need for handcrafted summary statistics and can yield posterior approximations that more closely reflect the true posterior under suitable conditions. Despite these developments, full data ABC approaches have not yet been widely applied to model selection problems. This paper seeks to address this gap by investigating the performance of ABC with statistical distances in model selection. Through simulation studies and an application to toad movement models, this work explores whether full data approaches can overcome the limitations of summary statistic-based ABC for model choice.
Keywords: model choice, distance metrics, full data approaches
Reference: C. Grazian, Approximate Bayesian Computation with Statistical Distances for Model Selection, preprint at ArXiv:2410.21603, 2025

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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!)

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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 inference” on 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

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the Harvard and Brown school of computer science

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on October 2, 2025 by xi'an

 “In the late 1980s, LeCun, then a researcher at AT&T Bell Labs, developed a powerful neural network that learned to recognise handwritten zip codes by training on thousands of examples. A parallel development soon unfolded at Harvard and Brown. In 1995, Zhu and a team of researchers there started developing probability-based methods that could learn to recognise patterns and textures (…) and even generate new examples of that pattern. These were not neural networks: members of the “Harvard-Brown school”, as Zhu called his team, cast vision as a problem of statistics and relied on methods such as “Bayesian inference” and “Markov random fields”. The two schools spoke different mathematical languages and had philosophical disagreements. But they shared an underlying logic – that data, rather than hand-coded instructions, could supply the infrastructure for machines to grasp the world and reproduce its patterns – that exists in today’s AI systems such as ChatGPT.” 

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