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 Monte Carlo methods
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'anISBA Satellite Meeting 2026 [overview]
Posted in pictures, Statistics, Travel, University life with tags conference, group picture, Institute of Statistical Mathematics, ISM, Japan, MCMC, Monte Carlo methods, poster session, privacy, Tachikawa, Tokyo, workshop on July 30, 2026 by xi'an
At the Akaike Guest House
Posted in pictures, Statistics, Travel, University life with tags AIC, Akaike's criterion, guest house, Hiroki Akaike, information geometry, Institute of Statistical Mathematics, ISBA, Japan, jatp, Monte Carlo methods, privacy, satellite workshop, Tachikawa, Tokyo on July 26, 2026 by xi'an
Information Geometry, Privacy and Monte Carlo workshop, ISM, 6-7 July 2026
Posted in Mountains, pictures, Statistics, Travel, University life with tags ABC, diffusion model, exponential families, Fuji-san, ICML 2026, Institute of Statistical Mathematics, ISM, Japanese Alps, Japanese cuisine, Japanese patisserie, lumpable Markov chain, manifold exploration, MCMC, Monte Carlo methods, normalising flow, O'Bayes, Ocean, Oscars (Academy Awards), particle filters, phase transition, Pitman-Yor process, poster session, privacy, RIKEN, spectral gap, sumo, supercomputer, Tachikawa, tofu, Tokyo on July 8, 2026 by xi'an
Although some of the participants of the workshop left for ICML²⁶ or the 4th Bayesian Nonparametrics networking workshop, both taking place in Seoul this week, the following days of the workshop were as intense and captivating as the first two, with a return to MCMC “basics” but also more geometrical and maethematical aspects.
To wit, Radu Craiu talked on MCMC for DAG processes with revisiting the landmark paper of Geyer & Møller (1994) on replacing discrete time MCMC with a birth & death process and cutting on complexity by restricted set imposing some edges, set from a redetermined run. Galin Jones presented some (novel) Lower bounds on the rate of convergence for accept-reject-based Markov chains in Wasserstein and total variation distances, showing the massive dependence of the convergence rates on the scaling factors of the proposal, especially in relation with the data size n when considering posterior targets. James Flegal discussed Simultaneous confidence bands for (MC)MC simulations that aimed at returning a confidence band on marginal density estimates; it reminded me of our 2005 simultaneous coverage paper with Wilfrid Kendall and Jean-Michel Marin and got me wondering why not going full Bayes by adopting a GP prior modelling.
Michiko Okudo spoke about Applications of information geometry to Bayesian prediction and estimation in curved exponential families, returning to point estimation with a mention of Marchand & Strawderman (2025)! Marta Catalano presented results on Distances on random measures for Bayesian nonparametrics, involving random measures like Dirichlet processes, that was connected with Hugo Lavenant’s talk at ISBA, but more focussed on the mathematical aspects albeit algorithmic aspects were mentioned. With highly intuitive arguments (making the accronym WoW for Wasserstein on Wasserstein quite appropriate!).
Takemasa Miyoshi made a presentation of the Osaka Expo 2025 Weather [prediction] on Fugaku: Synergizing Big Data Assimilation and AIRIKEN, with impressive predictive abilities achieved using RIKEN super-computer (but no technical details). Björn Sprung exposed how they obtained Dimension-independent MCMC [convergence speed] on the sphere, using retroprojections of random walks outside the sphere (as in Frederica’s talk yesterday), which comes as a surprise given the deterioration of random walk performances with increasing dimensions.
Geoffrey Wolfer’s Characterization of Exponential Families of Lumpable Stochastic Matrices was a very mathematical talk set firmly in the Japanese probability school, going too fast with too many new definitions for my abilities (and attention span) but setting the scene for exponential families on stochastic matrices and being one of the rate cases I eve rsaw lumpability à la Kemeny & Snell (1983) mentionned! Daniel Paulin followed with Stochastic gradient Langevin dynamics: convergence and bias, via an UBU algorithm using splitting integrators that sound very much like the leapfrog for an HMC with unscented Langevin steps where the gradient is replaced with an unbiased estimator (connecting to the poster of Jack Jewson on Sunday, when he mentioned the opposition between pseudo-marginal MCMC, requiring an unbiased estimator of the target, and schemes using the log-target, for which unbiased estimators of the log can be used). Shahab Asoodeh concluded Monday with Recent Advances in Metropolis-Hastings Algorithms, actually developing multi-marginal coupling with freely coupling chains.
On the final morning, Weiming Feng showed results about a Faster mixing of the Jerrum-Sinclair chain, reminding me of the 1989 paper, with a Metropolis algorithm on graphs allowing for specific mixing time results with spectral gap and log-Sobolev inequalities (and a Poincáre typo!). Michael Choi produced convergence properties by Optimising two-block averaging kernels to speed up Markov chains, with a (rather formal) Gibbs sampler on orbits (in a finite state space) again connecting to Jerrum.
Yuga Iguchi discussed Diffusion models for high-dimensional clustered data: Intrinsic-dimension adaptivity via Bayesian classification, producing a rigorous characterisation of the phase transition property of their diffusion denoising probabilistic model when the target is a mixture with separation constraints on the components, phase transition meaning that eventually concentrating on a single cluster as the forward diffusion moves toward pure noise. (Although being fully awake, having mostly recovered from the longest jetlag period ever, I had trouble understanding the process per se.) Edric Tam discussed Fundamental Limits to Neural Monte Carlo by returning to standard variance reduction techniques like stratifying and antithetic-ying (!) and applying normalising flows on them. Victor Elvira concluded the meeting by Rethinking self-normalized importance sampling, with a fun interlude of Eric Veach’s Oscars joke, but I unfortunately had to miss the end to gather my bags and leave for the Alps! But Victor should be in Paris in the Fall and hopfefully giving a talk at mostly Monte Carlo!
This workshop was most efficiently supported by the Institute of Statistical Mathematics and its staff, including over the weekend days! On a personal foodie note, the coffee breaks featured the same unbelievable matcha cakes (“Chez Kobe”) as at ISBA²⁶, we enjoyed a terrific full tofu dinner at Umenohana Tachikawa shop and there were plenty French (or pseudo-French) bakeries in Tachekima, enough to find rye (raimugi) bread for breakfast!
Information Geometry, Privacy and Monte Carlo workshop, ISM, 4-5 July 2026
Posted in Mountains, pictures, Statistics, Travel, University life with tags #ERCSyG, ABC, differential privacy, ERC Synergy Grant, federated learning, ICML 2026, Institute of Statistical Mathematics, ISM, manifold exploration, MCMC, Monte Carlo methods, O'Bayes, Ocean, particle filters, Pitman-Yor process, poster session, privacy, sumo, Tachikawa, Tokyo on July 6, 2026 by xi'anAfter the (exciting) variety and spread of ISBA²⁶, here we are at much more focussed (and single-track), if equally exciting, workshop at the ISM. (With many participants from ISBA²⁶.)
On Saturday afternoon, Ajay Jasra talked about Particle filtering for state-space models with low, degenerate noise, with specific measure issues I did not really get, since the manifold attached to the noise was known, but the projected density may prove a challenge. Manifolds were also central to Kenji Fukumizu’s talk on Learning manifold structure and density with score-based models learning scores as projectors to the manifold, although it was unclear to me how this was possible when the manifold is unknown. Christophe Andrieu presented Geometry informed selection in multiple proposal MCMC which stems from an early multi-proposal (1998) proposal by Radford Neal and uses a multivariate ranking procedure to quantify a measure of surprise for the current Markov chain value within the proposed ones. The crux for the efficiency of the approach may be in the choice of this ranking procedure. And Maria De Iorio talked about Efficient MCMC via similarity-driven proposals for discrete support targets, with similarities with ABC.
Completed with a human sized poster session where I reconnected with Spanish friends I had not seen for ages (by missing OBayes meetings).
On (pleasantly rainy) Sunday morning, Federica Milinanni detailled her Rapid mixing of stereographic MCMC for heavy-tailed sampling, essentially the same content as in Nagoya last FRiday, with a novel sub-Cauchy projection supposed to explore heavy tails better: while the regular stereographic projection turns the t-distribution with d degrees of freedom into a Uniform on the hypersphere, a sub-Cauchy projection turns the Cauchy into this uniform. In the privacy session I organised, Hongsheng Dai, member of our ERC Synergy project, presented an Online federated learning framework for classification, using DP as a criterion and achieving by adding noise to the loss function at each occurrence of the data production. Surprisingly increasing with the number of occurrences, not so much since the objective function keeps calling
Stefano Favaro described his Bayesian nonparametric privacy-preserving synthetic data generation method (for discrete data) that connects privacy protection and information preservation. (Incidentally I was unaware of the σ parameter of the Pitman-Yor process, which allows for a finite support when σ<0, but I cannot fathom the appeal of this extension, given the complete lack of connection between the positive and negative cases.) Surprisingly, non-parametric prediction does worse in terms of privacy, if not so surprising with discrete data since the predictive actually put weight on every datapoint. Resorting to mechanism informativity by Wasserman and Zhou (2010) (with a surprise mention of my friend Arnaud Guilin!). And Joshua Bon gave his Persuasive Privacy talk of last Tuesday (to be re-repeated two days later at ICML²⁶ in Seoul!). Except for changing the audience game from croissants to sumo wrestlers! (What will it be in Seoul!?) And adding much more details on the foundational elements of persuasive privacy.
The poster session was similarly enjoyable, even though I did not manage to get through all posters.
