The paper Prequential posteriors Shreya Roy wrote as part of her PhD thesis at Warwick U, under the supervision of Rito Dutta, Richard Everitt and myself, got published on-line after earlier acceptance by the Japanese Journal of Statistics and Data Science, an official journal of the Japanese Federation of Statistical Science Associations. Coïncidental but unrelated to my Akaike Memorial Lecture prize. The paper will be part of a special issue on Recent Advances in Dynamical Monte Carlo Methods. Congrats to Shreya, soon to defend her viva in Warwick!
Archive for special issue
prequential posteriors in the Japanese Journal of Statistics and Data Science
Posted in Books, Statistics, Travel, University life with tags Akaike Lecture, data assimilation, deep generative forecasting models, defense, generalised Bayesian inference, generative models, intractable likelihood, Japan, Japanese Federation of Statistical Science Associations, Japanese Journal of Statistics and Data Science, Monte Carlo methods, prequential loss, prequential priors, publication, sequential Monte Carlo, special issue, viva on September 22, 2026 by xi'anspecial issue of Statistica Sinica on sequential Monte Carlo
Posted in Books, Statistics, University life with tags debiasing, Kalman filter, particle filter, resampling, sequential Monte Carlo, SMC, special issue, Statistica Sinica on May 24, 2024 by xi'an
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
under the wave of climate change [cover]
Posted in Books, Kids, pictures with tags Birgit Schössow, climate change, cover, Hokusai, Japanese art, Japanese painting, Kanagawa, special issue, The New Yorker, Thirty-six Views of Mount Fuji, ukiyo-e, woodprint on January 26, 2023 by xi'an
approximate Bayesian inference [survey]
Posted in Statistics with tags ABC, Approximate Bayesian computation, Bayesian statistics, CREST, entropy, expectation-propagation, Gibbs posterior, Langevin Monte Carlo, Laplace approximations, machine learning, Markov chain Monte Carlo, MCMC, PAC-Bayes, RIKEN, sequential Monte Carlo, special issue, survey, Tokyo, variational approximations on May 3, 2021 by xi'an
In connection with the special issue of Entropy I mentioned a while ago, Pierre Alquier (formerly of CREST) has written an introduction to the topic of approximate Bayesian inference that is worth advertising (and freely-available as well). Its reference list is particularly relevant. (The deadline for submissions is 21 June,)
