Archive for QUT
Bayes on the Beach 2026 (University of Wollongong, NSW, 9-11 Feb.)
Posted in Kids, Mountains, pictures, R, Running, Statistics, Travel, University life, Wines with tags Australia, Bayes on the Beach, Bayesian computational methods, Bayesian statistics, beach, BoB, collaborative workshop, conference, Dharawal language, Grand Pacific Drive, Illawarra escarpment, New South Wales, Pacific Ocean, Queensland University of Technology, QUT, University of Wollongong on September 18, 2025 by xi'an
against modern sl[AI]very
Posted in Kids, R, Statistics, University life with tags AI, AIMS, artificial intelligence, data mining, ethical machine learning, hackathon, Human Rights, IRC, LLM, machine learning research, modern slavery, open source, Paso Libres, QUT, software development, UNESCO on July 2, 2025 by xi'an
statistical accuracy of neural posterior and likelihood estimation
Posted in pictures, Running, Statistics, Travel, University life with tags ABC, ABC consistency, Approximate Bayesian computation, Australia, Bayesian synthetic likelihood, Biometrika, Brisbane, Monash University, neural density estimator, neural posterior estimation, QUT, St Kilda, summary statistics on March 17, 2025 by xi'an
As I have been aiming at mentioning this news for quite a while, David Frazier, Ryan Kelly, Christopher Drovandi, and David Warne arXived last November a paper that parallels our paper (with David and Gael) on ABC consistency and some earlier papers of theirs for synthetic likelihood in the case of neural posterior approximations, under similar conditions (see, e.g., Assumptions 1 and 2), with potential reduced computational cost in some situations.
“NLE requires additional MCMC steps to produce a posterior approximation, whereas NPE produces a posterior approximation directly and does not require any additional sampling”
Convergence is achieved when the neural learning size grows fast enough with the sample size. And when the tolerance decreases fast enough with respect to the convergence rate of the summary statistic. Two options are possible, that is either approximating the likelihood and then exploiting this approximation in an MCMC algorithm, or directly approximating the posterior distribution, as a function of of the summary statistic Sn (rather than for the observed S⁰n), with arguments favouring the second option.
“if the intractable posterior Π(· | Sn) is asymptotically Gaussian a nd calibrated, then so long as νnγN = o(1), the NPE is also asymptotically Gaussian and calibrated”
where γN denotes the rate at which the neural approximation of the posterior converges to the ideal posterior (for the Kullback-Leibler divergence) in N the size of the learning sample. And νn is the rate of convergence of the statistic Sn to its asymptotic mean. The convergence result does not make explicit assumptions on the class of neural posteriors, but it requires that the observed statistic must fit within the range of the simulated values (a possibility illustrated in the paper with an MA(2) model that was already used in several of our papers (as I noticed when giving an ABC masterclass in Warwick this very week).
“While neural methods and normalizing flows are common choices for the approximating class Q, the diversity of such methods, along with their complicated tuning and training regimes, makes establishing theoretical results on the rate of convergence, γN, difficult”
Under stronger and hard to check assumptions, namely on the minimaxity of the posterior density estimator within the class of locally β-Hölder functions, they recover a closed form γN . Which unravels how N should be chosen (with a surprising addition of the dimensions of the parameter θ and of the summary Sn. With a resulting explosion in the theoretical minimal value of N one should use. (And decent performances of the method with smaller values of N!) Concerning minimaxity, I have no intuition how this impacts the sparseness (lack thereof) of the neural networks that can be used.
I am wondering at strategies to remove superfluous statistics since their dimension matters so much and in detecting or evaluating the misspecification (or its complement, the compatibility, as discussed on page 31). But all in all this paper represents a massive addition to the consistency results for approximate Bayesian inference methods!
futuristic statistical science [editorial]
Posted in Books, Kids, Statistics, University life with tags ABC, Australia, Bayes on the Beach, Bayesian computing, Bayesian design, BRAG, BUGS, ChatGPT, experimental design, Gold Coast, large language models, PDMP, quantum computer, Queensland, QUT, Schrödinger bridge, SMC, special issue, Statistical Science, statistical software, Surfers Paradise, Thomas Bayes, Valencia 4 on January 13, 2024 by xi'an
This special issue of Statistical Science is devoted to the future of Bayesian computational statistics, from several perspectives. It involves a large group of researchers who contributed to collective articles, bringing their own perspectives and research interests into these surveys. Somewhat paradoxically, it starts with the past—and a conference on a Gold Coast beach. Martin, Frazier, and Robert first submitted a survey on the history of Bayesian computation, written after Gael Martin delivered a plenary lecture at Bayes on the Beach, a conference held in November 2017 in Surfers Paradise, Gold Coast, Queensland, and organised by Bayesian Research and Applications Group (BRAG), the Bayesian research group headed by Kerrie Mengersen at the Queensland University of Technology (QUT). Following a first round of reviews, this paper got split into two separate articles, Computing Bayes: From Then ‘Til Now , retracing some of the history of Bayesian computation, and Approximating Bayes in the 21st Century, which is both a survey and a prospective on the directions and trends of approximate Bayesian approaches (and not solely ABC). At this point, Sonia Petrone, editor of Statistical Science, suggested we had a special issue on the whole issue of trends of interest and promise for Bayesian computational statistics. Joining forces, after some delays and failures to convince others to engage, or to produce multilevel papers with distinct vignettes, we eventually put together an additional four papers, where lead authors gathered further authors to produce this diverse picture of some incoming advances in the field. We have deliberated avoided topics which have excellent recent reviews— such as Stein’s method, sequential Monte Carlo, piecewise deterministic Markov processes— and topics which are still in their infancy, such as the relationship of Bayesian approaches to large language models (LLMs) and foundation models.
Within this issue, Past, Present, and Future of Software for Bayesian Inference from Erik Štrumbelj & al covers the state of the art in the most popular Bayesian software, reminding us of the massive impact BUGS has had on the adoption of Bayesian tools since its early introduction in the early 1990s (which I remember discovering at the Fourth Valencia meeting on Bayesian statistics in April 1991). With an interesting distinction between first and second generations, and a light foray of the potential third generation, maybe missing the role of LLMs in coding that are already impacting the approach to computing and the less immediate revolution brought by quantum computing. Winter & al.’s The Future of Bayesian Computation [TITLE TO CHANCE] is making a link with machine learning techniques, without looking at the scariest issue of how Bayesian inference can survive in a machine learning world! While it produces an additional foray into the blurry division between proper sampling (à la MCMC) and approximations, additional to the historical Martin et al. (2024), it articulates these aspects within a (deep) machine learning perspective, emphasizing the role of summaries produced by generative models exploiting the power of neural network computation/optimization. And the pivotal reliance on variational Bayes, which is the most active common denominator with machine learning. With further entries on major issues like distributed computing, opening on the important aspect of data protection and guaranteed privacy. We particularly like the clinical presentation of this paper with attention to automation and limitations. Normalizing flows actually link this paper with Heng, Bortoli and Doucet’s coverage of the Schrödinger bridge, which is a more focussed coverage of recent advances on possibly the next generation of posterior samplers. The final paper, Bayesian experimental design by Rainforth & al., provides a most convincing application of the methods exposed in the earlier papers in that the field of Bayesian design has hugely benefited from the occurrence of such tools to become a prevalent way of designing statistical experiments in real settings.
We feel the future of Bayesian computing is bright! The Monte Carlo revolution of the 1990s continues to be a huge influence on today’s work, and now is complemented by an exciting range of new directions informed by modern machine learning.
Dennis Prangle and Christian P Robert
Bayes on the Beach²⁴
Posted in Statistics, Travel with tags Australia, Bayes on the Beach, Bayesian conference, beach, down under, Gold Coast, ISBA, Queensland, Queensland University of Technology, QUT, Surfers Paradise, surfing on August 29, 2023 by xi'an
