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Approximate Bayesian Computation with Statistical Distances for Model Selection [OWABI, 27 Nov]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , on November 17, 2025 by xi'an

The next OWABI seminar is delivered by Clara Grazian (University of Sidney), who will talk about “Approximate Bayesian Computation with Statistical Distances for Model Selection” on Thursday 27 November at 11am UK time:

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. 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. 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. 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. 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. 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. 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.
Keywords: model choice, distance metrics, full data approaches
Reference: C. Grazian, Approximate Bayesian Computation with Statistical Distances for Model Selection, preprint at ArXiv:2410.21603, 2025

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exceptional OWABI web/sem’inar [19 June, BayesComp²⁵]

Posted in pictures, Statistics, Travel, Uncategorized, University life with tags , , , , , , , , , , , , , , on June 10, 2025 by xi'an


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 Singapore time (1pm in Tórshavn) and two talks, one by Filippo Pagani on

Approximate Bayesian Fusion
Bayesian Fusion is a powerful approach that enables distributed inference while maintaining exactness. However, the approach is computationally expensive. In this work, we propose a novel method that incorporates numerical approximations to alleviate the most computationally expensive steps, thereby achieving substantial reductions in runtime. Our approach retains the flexibility to approximate the target posterior distribution to an arbitrary degree of accuracy, and is scalable with respect to both the size of the dataset and the number of computational cores. Our method offers a practical and efficient alternative for large-scale Bayesian inference in distributed environments.
and one by Maurizio Filippone on
GANs Secretly Perform Approximate Bayesian Model Selection
Generative Adversarial Networks (GANs) are popular models achieving impressive performance in various generative modeling tasks. In this work, we aim at explaining the undeniable success of GANs by interpreting them as probabilistic generative models. In this view, GANs transform a distribution over latent variables Z into a distribution over inputs X through a function parameterized by a neural network, which is usually referred to as the generator. This probabilistic interpretation enables us to cast the GAN adversarial-style optimization as a proxy for marginal likelihood optimization. More specifically, it is possible to show that marginal likelihood maximization with respect to model parameters is equivalent to the minimization of the Kullback-Leibler (KL) divergence between the true data generating distribution and the one modeled by the GAN. By replacing the KL divergence with other divergences and integral probability metrics we obtain popular variants of GANs such as f-GANs, Wasserstein-GANs, and Maximum Mean Discrepancy (MMD)-GANs. This connection has profound implications because of the desirable properties associated with marginal likelihood optimization, such as (i) lack of overfitting, which explains the success of GANs, and (ii) allowing for model selection, which opens to the possibility of obtaining parsimonious generators through architecture search.

These talks will be delivered on-site and on-line, as a Zoom visio-conference.

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next OWABI webinar [24 April]

Posted in pictures, Statistics, Uncategorized, University life with tags , , , , , , , , , , , , on April 16, 2025 by xi'an


The next One World Approximate Bayesian Inference (OWABI) Seminar is scheduled on Thursday the 24th of April at 11am UK time (12am CET) with the speaker being Ayush Bharti (Aalto University), who will talk about

“Cost-aware simulation-based inference “

Abstract: Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computational cost of simulating data from complex models, and the fact that this cost often depends on parameter values. We therefore propose cost-aware SBI methods which can significantly reduce the cost of existing sampling-based SBI methods, such as neural SBI and approximate Bayesian computation. This is achieved through a combination of rejection and self-normalised importance sampling, which significantly reduces the number of expensive simulations needed. Our approach is studied extensively on models from epidemiology to telecommunications engineering, where we obtain significant reductions in the overall cost of inference. .

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next OWABI webinar [27 March]

Posted in pictures, Statistics, Uncategorized, University life with tags , , , , , , , , , , , , , , , , on March 25, 2025 by xi'an


The next One World Approximate Bayesian Inference (OWABI) Seminar is scheduled on Thursday the 27th of March at 11am UK time (12am CET) with the speaker being Meïli Baragatti (Université de Montpellier)

 Approximate Bayesian Computation with Deep Learning and Conformal Prediction

Abstract: Approximate Bayesian Computation (ABC) methods are commonly used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Classical ABC methods are based on nearest neighbour type algorithms and rely on the choice of so-called summary statistics, distances between datasets and a tolerance threshold. Recently, methods combining ABC with more complex machine learning algorithms have been proposed to mitigate the impact of these “user-choices”. In this talk, I will present you the first, to our knowledge, ABC method completely free of summary statistics, distance, and tolerance threshold. Moreover, in contrast with usual generalisations of the ABC method, it associates a confidence interval (having a proper frequentist marginal coverage) with the posterior mean estimation (or other moment-type estimates). This method, named ABCD-Conformal, uses a neural network with Monte Carlo Dropout to provide an estimation of the posterior mean (or other moment type functionals), and conformal theory to obtain associated confidence sets. I will compare its performances with other ABC methods on several examples, and show you that it is efficient for estimating multidimensional parameters, while being “amortised”.

Keywords: simulation-based inference, approximate Bayesian computation, neural posterior estimation, convolutional neural networks, dropout, conformal prediction

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next OWABI webinar [27 Feb]

Posted in pictures, Statistics, Uncategorized with tags , , , , , , , , , , on February 21, 2025 by xi'an


The next One World Approximate Bayesian Inference (OWABI) Seminar is scheduled on Thursday, 27 February at 11am UK time, with the speaker being Ayush Bharti (Aalto University),

Cost-aware simulation-based inference

Abstract: Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computational cost of simulating data from complex models, and the fact that this cost often depends on parameter values. We therefore propose cost-aware SBI methods which can significantly reduce the cost of existing sampling-based SBI methods, such as neural SBI and approximate Bayesian computation. This is achieved through a combination of rejection and self-normalised importance sampling, which significantly reduces the number of expensive simulations needed. Our approach is studied extensively on models from epidemiology to telecommunications engineering, where we obtain significant reductions in the overall cost of inference.

Keywords: simulation-based inference, approximate Bayesian computation, neural posterior estimation, neural likelihood estimation, importance sampling

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