Dear all, The next OWABI www.warwick.ac.uk/owabi <http://www.warwick.ac.uk/owabi> seminar is quickly approaching. Our next speaker is Shreya Sinha Roy <https://warwick.ac.uk/fac/sci/statistics/staff/research_students/sinharoy/> (University of Warwick), who will talk about "Prequential posteriors" on *Thursday the 26th February at 11am UK* time, with an abstract reported below. The talk will be streamed on the OWABI MS Team channel General | OWABI Seminar: One World Approximate Bayesian Inference Seminar | Microsoft Teams <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 below *Join: *_https://teams.microsoft.com/meet/32189045171547?p=HltIhSAIPRyOBZfj2M <https://teams.microsoft.com/meet/32189045171547?p=HltIhSAIPRyOBZfj2M>_ Meeting ID: 321 890 451 715 47 Passcode: Ek7Su3TJ We are looking forward to seeing you at the next OWABI seminar, Best, Umberto on the behalf of the OWABI seminar organisers *Speaker*: Shreya Sinha Roy <https://warwick.ac.uk/fac/sci/statistics/staff/research_students/sinharoy/> (University of Warwick) *Title*: Prequential posteriors *Abstract:* Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning. Deep generative forecasting models (DGFMs) have shown excellent performance in these areas, but assimilating data into such models is challenging due to their intractable likelihood functions. This limitation restricts the use of standard Bayesian data assimilation methodologies for DGFMs. To overcome this, we introduce prequential posteriors, based upon a predictive-sequential (prequential) loss function; an approach naturally suited for temporally dependent data which is the focus of forecasting tasks. Since the true data-generating process often lies outside the assumed model class, we adopt an alternative notion of consistency and prove that, under mild conditions, both the prequential loss minimizer and the prequential posterior concentrate around parameters with optimal predictive performance. For scalable inference, we employ easily parallelizable wastefree sequential Monte Carlo (SMC) samplers with preconditioned gradient-based kernels, enabling efficient exploration of high-dimensional parameter spaces such as those in DGFMs. We validate our method on both a synthetic multi-dimensional time series and a real-world meteorological dataset; highlighting its practical utility for data assimilation for complex dynamical systems. *Keywords*: diffusion models, simulation based inference, sequential methods. *Reference*: S. S. Roy, R. Everitt, C. Robert, R. Dutta. Prequential posteriors <https://arxiv.org/pdf/2511.17721v1>. Preprint at ArXiv:2511.17721, 2025. -- ____________________________________________________________________ Umberto Picchini, Full Professor of Mathematical Statistics https://umbertopicchini.github.io/ , Bluesky: @upicchini.bsky.social