Archive for Bardonecchia

BayesComp²⁰²⁷ mirror in Aussois

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , on August 13, 2026 by xi'an

Here we go! We have just completed the webpage for the BayesComp 2027 mirror conference in Aussois, French Alps, and launched the call for contributions.

This meeting mirrors the main BayesComp 2027 conference, held in Texas on the same week. As such, it will broadcast live and delayed sessions from the main conference, including plenary sessions, host local talk and poster sessions (to be broadcasted), and more generally provide an alternative forum for researchers in Bayesian computational statistics who cannot attend BayesComp 2027. As discussed earlier on the ‘Og, and following a debate launched at the ISBA 2024 World Meeting in Venezia, ISBA is now considering mirror (or multi-hub) meetings as a possible way to address accessibility, sustainability, and inclusiveness issues in its scientific meetings. 

The scientific committee will select presentations soon after the submission deadline, on 30 November 2026. Note that due to the capacity of the CAES Paul Langevin conference centre in Aussois the number of participants will be capped at 200. (Travel to Aussois by train to Modane is easy, thanks to direct, fast trains from Paris (4h), Torino (1h), and Lyon (2h). And by car if need be, since Modane is the French exit of the Tunnel du Fréjus (aka Traforo Alpino del Fréjus) linking France and Italy, the Italian exit being Bardonecchia.)

Important Dates

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OWABI@BioInference2025 [29 May]

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on May 13, 2025 by xi'an

The next OWABI webinar is going to be quite special, consisting of two selected talks livestreamed from BioInference 2025, a conference on mathematical modelling and inference on (broadly speaking) biological system, taking place in Bardonecchia, Piedmont. The talks will take place on 29 May, 11am CEST (10am BST). The talks will be streamed on the OWABI MS Team Channel as usual.

1st OWABI Talk: 10-10.30am UK time

Speaker: Andrew Golightly (Durham University)

Title: Accelerating Bayesian inference for stochastic epidemic models using incidence data

Abstract: This work considers the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed length. The most natural Markov jump process representation of the model is eschewed for reasons of computational efficiency, and replaced by a stochastic differential equation representation. This is further approximated to give a tractable Gaussian process, that is, the linear noise approximation (LNA). Unless the observation model linking the LNA to data is both linear and Gaussian, the observed data likelihood remains intractable. Unlike previous approaches that use the LNA in this setting, two approaches for marginalising over the latent process are considered: a correlated pseudo-marginal method and analytic marginalisation via a Gaussian approximation of the noise model. These approaches are compared using synthetic data with the best performing method applied to real data consisting of removal incidence of Oak Processionary moth nests in Richmond Park, London.

2nd OWABI Talk: 10.30-11am

Speaker: Henrik Häggström (Chalmers University)

Title: Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology

Abstract: We propose a novel methodology for Bayesian inference in hierarchical mixed-effects models. By building on our work, we construct a simulation-based inference (SBI) framework that is highly scalable, where amortized approximations to the likelihood and the parameters posterior are first obtained, and these are rapidly refined for each individual dataset, to ultimately approximate the parameters posterior across many individuals. Unlike the current state-of-art SBI methods, which use neural networks, our approximations are expressed via Gaussian mixture models, leading to easily trainable, parsimonious yet expressive surrogate models of both the likelihood function and the posterior distribution. The methodology is exemplified via stochastic differential equation mixed-effects models to describe translation kinetics after mRNA transfection, however the methodology is general and can accommodate other types of stochastic and deterministic models. We compare our approximate inference with exact pseudomarginal inference and show that our methodology is fast and competitive.

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