Dear all,
the next OWABI www.warwick.ac.uk/owabihttp://www.warwick.ac.uk/owabi seminar is quickly approaching, with two talks live from BioInference 2025 (https://bioinference.github.io/2025/).
The seminars will be on Thursday the 29th May, at 10am UK time (note the different time with respect to previous seminars), given by
* Andrew Golightly (Durham University), Accelerating Bayesian inference for stochastic epidemic models using incidence data * Henrik Häggström (Chalmers University), Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology
with abstracts reported below.
The talks will be streamed on the OWABI MS Team Channelhttps://teams.microsoft.com/l/team/19%3AdhZ_4e_XLNJzCXPAMzTvT6BZ5KShEETkd_wtTY52VI81%40thread.tacv2/conversations?groupId=9c061d11-f88c-4cee-938f-bf40e7393879&tenantId=09bacfbd-47ef-4465-9265-3546f2eaf6bc https://teams.microsoft.com/l/team/19%3AdhZ_4e_XLNJzCXPAMzTvT6BZ5KShEETkd_wtTY52VI81%40thread.tacv2/conversations?groupId=9c061d11-f88c-4cee-938f-bf40e7393879&tenantId=09bacfbd-47ef-4465-9265-3546f2eaf6bc , at the link https://teams.microsoft.com/l/meetup-join/19%3adhZ_4e_XLNJzCXPAMzTvT6BZ5KShE... Meeting ID: 323 917 219 730 3 Passcode: Fk9JG3eB
1st OWABI Talk: 10-10.30am UK time
Speaker: Andrew Golightlyhttps://www.durham.ac.uk/staff/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 Seminar: 10.30-11am UK time
Speaker: Henrik Häggströmhttps://research.chalmers.se/en/person/henhagg (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 [1], 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.
We are looking forward to seeing you at the next OWABI seminar(s), best, Massimiliano on the behalf of the OWABI Organisers
------ Dr. Massimiliano Tamborrino Reader (Associate Professor) and WIHEA Fellow Department of Statistics University of Warwick https://warwick.ac.uk/tamborrino