Archive for Bayes on the Beach
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
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
Basque thesis defence [Bayes almost on the beach]
Posted in Books, Kids, pictures, Statistics, Travel, University life with tags Anglet, Basque country, Bayes on the Beach, Bayesian Analysis, Bayesian space-time models, Bayonne, Biarritz, chromatic sampler, Gaussian processes, interweaving, jatp, MCMC, nearest neighbor Gaussian processes, plane picture, surfing, Teams on October 21, 2021 by xi'an
Yesterday morning I took part in a thesis defence (as a jury member) in the coastal city of Anglet, in the (French part of the) Basque Country. The PhD candidate was Sébastien Coube-Sisqueille, whom I did not know directly (although we had crossed paths at CIRM years ago and he had attended my MCMC course at ENSAE even more years ago). As it happened all other members of the committee, apart from Sébastien’s advisor, Benoît Liquet, were on Teams, being unable to travel to the Basque Country. Sébastien’s thesis is about MCMC strategies to accelerate convergence in spatial models represented as nearest neighbor Gaussian processes (NNGP), which relates to the earlier works of (X)XL on interweaving. (Unsurprisingly, the defence was successful and the candidate awarded his PhD!) Icing on the cake, I managed to take a dip in the Atlantic Ocean, before flying back to Paris for dinner, on a very warm afternoon (and slightly cooler water), thanks to Sébastien driving me to a nearby beach!
Computing Bayes: Bayesian Computation from 1763 to the 21st Century
Posted in Books, pictures, Statistics, Travel, University life with tags 1763, Australia, Bayes on the Beach, Bayesian computation, Monash University, survey, Thomas Bayes on April 16, 2020 by xi'an
Last night, Gael Martin, David Frazier (from Monash U) and myself arXived a survey on the history of Bayesian computations. This project started when Gael presented a historical overview of Bayesian computation, then entitled ‘Computing Bayes: Bayesian Computation from 1763 to 2017!’, at ‘Bayes on the Beach’ (Queensland, November, 2017). She then decided to build a survey from the material she had gathered, with her usual dedication and stamina. Asking David and I to join forces and bring additional perspectives on this history. While this is a short and hence necessary incomplete history (of not everything!), it hopefully brings some different threads together in an original enough fashion (as I think there is little overlap with recent surveys I wrote). We welcome comments about aspects we missed, skipped or misrepresented, most obviously!
