Archive for Bayesian computing
computing Bayes, with some approximations
Posted in Books, Statistics, University life with tags Approximate Bayesian computation, Australia, Bayesian computing, IMS, Institute of Mathematical Statistics, Melbourne, Monash University, publishing, Queensland, Surfers Paradise, survey, Victoria, Victoria wines on March 10, 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
BayesComp²⁵ in Singapore
Posted in pictures, Statistics, Travel with tags Arctic, BayesComp 2025, Bayesian computing, conference, equator, ISBA, Levi, National University Singapore, NUS, Singapore on August 28, 2023 by xi'an
trở về từ thành phố hồ chí minh
Posted in Books, pictures, Statistics, Travel, University life with tags ABC, Bayesian Analysis, Bayesian computing, HCMC, Ho Chi Minh City, MCMC, Saigon, summer school, UEH, Vietnam on August 10, 2023 by xi'an
While fast and furious (!), the days I spent in Ho Chi Minh City (at UEH, the university of economics of HCMC) were most enjoyable, from running very early in the relatively cool morning, by the nearby canal, often with others, to the group breakfast at a small food stall, or another, with a huge variety of dishes, to multiple discussions at the coffee and lunch breaks (with an unknown [by me] fruit every day!), to question sessions, despite my massive infodump, especially on the first lecture!, with the above picture taken during the ABC lecture, with Long Nguyen’s logical map between lectures, to group dinners with again a huge variety of dishes, and above all an incredibly friendly crowd of students and faculty that made days fly! The school was well-prepared with a reading program prior to it and a project dissertation posterior to it. Cảm ơn họ vì lòng tốt của họ!
computing Bayes 2.0
Posted in Books, Statistics, University life with tags Approximate Bayesian computation, arXiv, ASSA, Australia, Bayesian computing, MCMC, Monash University, Monte Carlo methods, Monte Carlo Statistical Methods, review, revision, survey on December 11, 2020 by xi'an
Our survey paper on “computing Bayes“, written with my friends Gael Martin [who led this project most efficiently!] and David Frazier, has now been revised and resubmitted, the new version being now available on arXiv. Recognising that the entire range of the literature cannot be encompassed within a single review, esp. wrt the theoretical advances made on MCMC, the revised version is more focussed on the approximative solutions (when considering MCMC as “exact”!). As put by one of the referees [which were all very supportive of the paper], “the authors are very brave. To cover in a review paper the computational methods for Bayesian inference is indeed a monumental task and in a way an hopeless one”. This is the opportunity to congratulate Gael on her election to the Academy of Social Sciences of Australia last month. (Along with her colleague from Monash, Rob Hyndman.)
