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.

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.

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!

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

Speaker: Filippo Pagani
Title: Approximate Bayesian Fusion

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

For abstracts see xianblog.wordpress.c...

More details:
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

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