Approximate Bayes Seminar

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.

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.

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

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.

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.

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.

Prequential posteriors 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...

The next OWABI seminar will be given by Shreya Sinha Roy (Warwick), who will talk about "Prequential posteriors" on Thursday the 26th February at 11am UK time.
arxiv.org/abs/2511.1...
www.warwick.ac.uk/owabi
MS Teams link: teams.microsoft.com/...

or superior performance to existing state-of-the-art methodssuch as Sequential Neural Posterior Estimation (SNPE).
Ref: Sharrock, Simons, Liu, Beaumont, Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models. PLMR. openreview.net/pdf?i...

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

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

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

Tomorrow's OWABI seminar by Louis Sharrock (UCL), who will talk about "Sequential Neural Score Estimation: Likelihood-free inference with conditional score base diffusion models" (29th Jan, 11am UK time). Join: teams.microsoft.com/...
Meeting ID: 388 990 396 214 49
Passcode: f3Dh22XL

Approximate Bayesian Computation with Statistical Distances for... Model selection is a key task in statistics, playing a critical role across various scientific disciplines. While no model can fully capture the complexities of a real-world data-generating...

Through simulation studies and an application to toad movement models, this work explores whether full data approaches can overcome the limitations of summary statistic-based ABC for model choice.

Paper: arxiv.org/abs/2410.2...

Teams details:
Meeting ID: 365 183 408 357 76
Passcode: 6Fg9nW7T

Despite these developments, full data ABC approaches have not yet been widely applied to model selection problems. This paper seeks to address this gap by investigating the performance of ABC with statistical distances in model selection.

Recent advancements propose the use of full data approaches based on statistical distances, offering a promising alternative that bypasses the need for handcrafted summary statistics and can yield posterior approximations that more closely reflect the true posterior under suitable conditions.

Approximate Bayesian computation (ABC) has emerged as a likelihood-free method and it is traditionally used with summary statistics to reduce data dimensionality, however this often results in information loss difficult to quantify, particularly in model selection contexts.

This is typically achieved by calculating posterior probabilities, which quantify the support for each model given the observed data. However, in cases where likelihood functions are intractable, exact computation of these posterior probabilities becomes infeasible.

Bayesian statistics offers a flexible framework for model selection by updating prior beliefs as new data becomes available, allowing for ongoing refinement of candidate models.

Abstract: Model selection is a key task in statistics, playing a critical role across various scientific disciplines. While no model can fully capture the complexities of a real-world data-generating process, identifying the model that best approximates it can provide valuable insights.

The next OWABI seminar www.warwick.ac.uk/owabi is quickly approaching!

Our next speaker is Clara Grazian (Sydney) www.sydney.edu.au/sc..., who will talk about "Approximate Bayesian Computation with Statistical Distances for Model Selection" on 27th Nov, 11am UK time.

The recording of my talk on 'Multilevel neural simulation-based inference' at the 'One World Approximate Bayesian Inference' seminar series is now available on YouTube.

Link: www.youtube.com/watch?v=hBWd...

We demonstrate through both theoretical analysis and extensive experiments that our method can significantly enhance the accuracy of SBI methods given a fixed computational budget.

The talk will be streamed on MS Teams:
Meeting ID: 358 173 458 006 0
Passcode: Vp2975vC

In this paper, we propose a novel approach to neural SBI which leverages multilevel Monte Carlo techniques for settings where several simulators of varying cost and fidelity are available.

However, the performance of neural SBI can suffer when simulators are computationally expensive, thereby limiting the number of simulations that can be performed.

Abstract: Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator.

Multilevel neural simulation-based inference Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences...

The 1st OWABI talk of the Season will be given by François-Xavier Briol (University College London). who will talk about "Multilevel neural simulation-based inference".

The session will run in a hybrid format, taking place live from BayesComp2025 - 8pm-9pm (Singapore time) - and will be located in room LT50 in the conference venue.

We look forward to seeing you online or in person!

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Filippo Pagani (University of Warwick): Approximate Bayesian Fusion
Maurizio Filippone (KAUST): GANs Secretly Perform Approximate Bayesian Model Selection

The talks will be live streamed from the following link:
monash.zoom.us/j/810...
Meeting ID: 810 5099 4376
Passcode: 137607

The final OWABI seminar www.warwick.ac.uk/owabi of the season will feature two selected talks and will take place live from BayesComp bayescomp2025.sg/! The talks will take place on Thursday the 19th June at 1pm UK time (note the different date and time!), and will be given by

exceptional OWABI web/sem’inar [19 June, BayesComp²⁵] Exceptionally, the next One World Approximate Bayesian Inference (OWABI) Seminar will be hybrid as it is scheduled to take place during BayesComp 2025 in Singapore, on Thursday 19 June at 8pm Singa…

Speaker: Filippo Pagani
Title: Approximate Bayesian Fusion

Speaker: Maurizio Filippone
Title: GANs Secretly Perform Approximate Bayesian Model Selection

For abstracts see xianblog.wordpress.c...