The paper Prequential posteriors Shreya Roy wrote as part of her PhD thesis at Warwick U, under the supervision of Rito Dutta, Richard Everitt and myself, got published on-line after earlier acceptance by the Japanese Journal of Statistics and Data Science, an official journal of the Japanese Federation of Statistical Science Associations. Coïncidental but unrelated to my Akaike Memorial Lecture prize. The paper will be part of a special issue on Recent Advances in Dynamical Monte Carlo Methods. Congrats to Shreya, soon to defend her viva in Warwick!
Archive for sequential Monte Carlo
prequential posteriors in the Japanese Journal of Statistics and Data Science
Posted in Books, Statistics, Travel, University life with tags Akaike Lecture, data assimilation, deep generative forecasting models, defense, generalised Bayesian inference, generative models, intractable likelihood, Japan, Japanese Federation of Statistical Science Associations, Japanese Journal of Statistics and Data Science, Monte Carlo methods, prequential loss, prequential priors, publication, sequential Monte Carlo, special issue, viva on September 22, 2026 by xi'anOWABI⁷, 26 February 2026: Prequential posteriors (11am UK time)
Posted in Books, Statistics, University life with tags ABC, approximate Bayesian inference, diffusion model, generalised Bayesian inference, generative model, OWABI, prequential loss, reinforcement learning, score function, sequential ABC, sequential Monte Carlo, simulation-based inference, SMC, University of Warwick, webinar on February 25, 2026 by xi'an
Speaker: Shreya Sinha Roy (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. P Robert, R. Dutta. Prequential posteriors. Preprint at ArXiv:2511.17721, 202
OWABI⁷, 29 January 2026: Sequential Neural Score Estimation (11am UK time)
Posted in Books, Statistics, University life with tags ABC, approximate Bayesian inference, diffusion model, generalised Bayesian inference, generative model, OWABI, score function, sequential Monte Carlo, simulation-based inference, University of Warwick, webinar 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.
Congrats, Dr. Marival!
Posted in Books, Statistics, University life with tags coauthors, importance Markov chains, importance MCMC, Institut Polytechnique de Paris, jury, Palaiseau, parallelisation, Paris-Saclay campus, PhD thesis, projection, sequential Monte Carlo, SMC, speed of convergence, Telecom ParisTech, thesis defence, variational approximations on March 26, 2025 by xi'an
mostly MC’nuary [10 Jan 2025]
Posted in Books, Statistics, University life with tags CMAP, experimental design, INRIA, Issy-les-Moulineaux, January, MCMC, Mostly MC, Mostly MCMC seminar, Ocean, Paris, PariSanté campus, PDMP, Porte de Versailles, scalable MCMC, seminar, sequential Monte Carlo on January 6, 2025 by xi'an
