Archive for approximate Bayesian inference

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. .

post-Bayes workshop at UCL [15 & 16 May 2025]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , on April 4, 2025 by xi'an

University College London (UCL) is organising a workshop on post-Bayes inference  and asked to post the announcement (despite my feeling that we have not entered the post-Bayes era!). So here it is:

Over the course of two days, they will host eight invited talks from leaders across the post-Bayesian landscape, spanning from PAC Bayes and generalised Bayes to predictive resampling and martingale posteriors. Alongside these, they will host six contributed talks, and a poster session to ignite discussion and innovation in our growing community. The workshop will complement the post-Bayesian seminar series.

Registration is now open, and they are actively accepting talk and poster submissions! (Deadline for submissions: April 11th, 2025.) Travel support for early career researchers will be available and announced closer to the date. See the website for more information. The workshop will take place in Bentham House, UCL, London.

[As a personal aside, we just learned that our proposal for an approximate(ly) Bayes workshop supported by ICMS (Edinburgh) and set on the magical Isle of Skye had been accepted! To be held in Spring 2026!]

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

amortized Bayesian mixture model

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , on February 7, 2025 by xi'an

A few days before the January OWABI, I read through Simon Kucharsky’s and Paul Bürkner’s paper, arXived on 17 January. Which proposed an amortized Bayesian inference (ABI) method, even though the ABI is not the same as in OWABI! The motivation for their work is to start from a (standard) mixture model where the components are not analytically tractable (but still parameterised). But a generative model nonetheless. As in the earlier reviewed paper (which was arXived on the same day), by MEJ Newman, the dual representation of the joint posterior p(θ,z|x) as p(z|x,θ)p(θ|x) and p(θ|z,x)p(z|x) is (over?) emphasized (albeit unclearly why!). ABI uses neural networks and more specifically normalising flows to approximate the posterior p(θ|x) from prior predictive samples (θ,x) (as in ABC), and then directly exploit the invertibility of said flows to generate from this approximate posterior. One interesting aspect of the modelling is the derivation of summary statistics in the design of the network, albeit mixture posteriors do not allow for dimension-reduced (Bayes) sufficient statistics (and a contradictory sentence that conditioning on the summaries “does not alter the target posterior”, p7). The resulting approximate posterior generator proves much much faster than running an MCMC, obviously, and furthermore adapt to handling a sequence of datasets. A second network is constructed to approximate p(z|x,θ), using the same summaries. The network parameters are estimated through losses, rather than in a Bayesian manner, with a default Kullback-Leibler version (18). I also fail to understand why the networks are trained over unconstrained parameters when all parameters could become unconstrained when using the adequate parameterisation. And am fairly surprised at the regression towards the ill-fated step of using ordered parameters to avoid label switching… But the main quandary remains the issue of assessing the approximation effect, despite experiments aiming at pacifying such worries. And similarities with Stan and BayesFlow.