Archive for One World ABC Seminar

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. .

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

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

next OWABI webinar [30 Jan]

Posted in pictures, Statistics, Uncategorized with tags , , , , , , , , , , , , on January 19, 2025 by xi'an


The next One World Approximate Bayesian Inference (OWABI) Seminar is scheduled on Thursday, the 30th January at 11am UK time, with the speaker being Paul Bürkner (TU Dortmund University),

Amortized Mixture and Multilevel Models

Abstract: Probabilistic mixture and multilevel models are central building blocks in Bayesian data analysis. However, they remain challenging to estimate and evaluate, especially when the involved likelihoods or priors are analytically intractable. Recent developments in generative deep learning and simulation-based inference have shown promising results in scaling up Bayesian inference through amortization. Against this background, we have developed specialized neural inference frameworks for estimating Bayesian mixture and multilevel models. The involved neural architectures are closely mirroring the probabilistic symmetries and conditional (in-)dependencies assumed by these models. This not only speeds up neural network training, but also enables amortized inference for new datasets of varying number of groups and sample sizes.

Keywords: Amortized Bayesian Inference; Neural Posterior Estimation; Probabilistic Factorization

OWAB [season] I

Posted in Statistics, University life with tags , , , , , , , , , , , , on November 23, 2024 by xi'an

Our “new” seminar series, the One World Approximate Bayesian Inference (OWABI) Seminar, will see its second OWABI talk given on Thursday 28 November at 11am UK time. The speaker is Jeremias Knoblauch (University College London), who will talk about

Post-Bayesian machine learning

Abstract: In this talk, I provide my perspective on the machine learning community’s efforts to develop inference procedures with Bayesian characteristics that go beyond Bayes’ Rule as an epistemological principle. I will explain why these efforts are needed, as well as the forms which they take. Focusing on some of my own contributions to the field, I will trace out some of the community’s most important milestones, as well as the challenges that lie ahead. Throughout, I will provide success stories of the field, and emphasise the new opportunities that open themselves up to us once we dare to go beyond orthodox Bayesian procedures.

Keywords: Generalised Bayes; robustness; Bayesian machine learning.