Archive for approximate Bayesian inference

ISBA³

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , on July 2, 2026 by xi'an

After another absurdly early run along the Sendai River, in higher humidity than yesterday, I had plenty of time to have breakfast and commute to the conference centre for our privacy section. (“Ours” since I organised and chaired the session, but also all speakers were related with our ERC OCEAN project.) With talks by Antoine Luciano (Dauphine), Shenggang Hu (Warwick) and Joshua Bon (formerly Dauphine), Shenggang presenting his work with Gareth on calibrating a DP, noisy, unbiased, version of Metropolis-Hastings. Given the other sessions highly competing with ours, this 100% OCEAN session was well-attended! My second morning session was the theory & method part of the Savage Award, with unfortunately two of the speakers stuck in the US for visa issues and presenting remotely. There have been years where this session suffered from competition from parallel sessions, but this time showed a quite decent attendance.

As most members of the scientific committee of the BayesComp mirror in Aussois were present, we went out for lunch at a nearby hitsumabushi restaurant to plan registrations and local sessions. Which proved very productive despite enjoying the fantastic eel dishes! If making me miss the beginning of the afternoon session… and then the whole session as the room on predictive Bayes was packed (and some speakers had already delivered on the Isle of Skye). I managed to get to Bottond Szabo’s Foundation lecture, with again common threads with Skye, and then returned to my rental as I was quickly crashing…

glorious morn on Loch Hourn [jatp]

Posted in Mountains, pictures, Travel, University life with tags , , , , , , , , , , , , on May 19, 2026 by xi'an

off to Sabhal Mòr Ostaig, Eilean Sgitheanach

Posted in Books, Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , on May 17, 2026 by xi'an

OWABI⁷, 25 March 2026: Robust Simulation Based Inference (10am EST time)

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , on March 9, 2026 by xi'an

Speaker:  Larry Wasserman (Carnegie Mellon University)

Title: Robust Simulation Based Inference
Abstract: Simulation-Based Inference (SBI) is an approach to statistical inference where simulations from an assumed model are used to construct estimators and confidence sets. SBI is often used when the likelihood is intractable and to construct confidence sets that do not rely on asymptotic methods or regularity conditions. Traditional SBI methods assume that the model is correct, but, as always, this can lead to invalid inference when the model is misspecified. This paper introduces robust methods that allow for valid frequentist inference in the presence of model misspecification. We propose a framework where the target of inference is a projection parameter that minimizes a discrepancy between the true distribution and the assumed model. The method guarantees valid inference, even when the model is incorrectly specified and even if the standard regularity conditions fail. Alternatively, we introduce model expansion through exponential tilting as another way to account for model misspecification. We also develop an SBI based goodness-of-fit test to detect model misspecification. Finally, we propose two ideas that are useful in the SBI framework beyond robust inference: an SBI based method to obtain closed form approximations of intractable models and an active learning approach to more efficiently sample the parameter space.
Keywords: Exponential tilting, model misspecification, robust inference, simulation based inference, valid inference.
Reference: Lorenzo Tomaselli, Valérie Ventura, Larry Wasserman. Robust Simulation Based Inference. Preprint at ArXiv:2508.02404

OWABI⁷, 26 February 2026: Prequential posteriors (11am UK time)

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , 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