Archive for cut models

ISBA⁵

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , on July 4, 2026 by xi'an

After a pleasant run under the pouring rain (noisy rain that had not helped with my sleep or lack thereof), I attended both morning episodes of Calibrated Bayes, with a range of interesting, mostly novel, questions and solutions. And rekindling my interrogations about overparameterised models and model misspecification within a Bayesian framework. And the elusive notion of outliers.

The noon discussion around our report as the committee on the future of ISBA conferences went on rather well, given the circumstances, with about 50 participants with ideas and opinions on the opportunity of splitting the ISBA World into multiple hubs. And on the practical difficulties. (Despite the itch to do it, I did not intervene with my counter-objections to most of the raised objections! Nor mentioned BayesComp in Aussois.) Participants of the (unofficial) 2021 mirror in Marseille brought some welcomed support!

Daniele Durante’s Bayarri lecture on skew symmetric approximations was also attuned to the approximation spirit of the morning. The symmetrisation of the target reminded me of our (unpublished) folded method. With the (unrelated) question of the statistical meaning of a higher order Laplace expansion. And the calibration of this skewed approximation.

The final session on Cut Bayes couldn’t be missed!, since this has been an interest of mine’s since MCMSki IV in Chamonix! With an automated cut model construction by Robert Goude and an optimised choice of generalised Bayes posteriors by David Nott.

BayesComp 2025.2

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on June 19, 2025 by xi'an


The main BayesComp²⁵ conference started with Pierre Jacob’s plenary talk on his recent advances on coupling for unbiased MCMC—currently ERC grantee on that topic—.  Raising lazy questions like using a different target or transition kernel for the second chain in the coupling, connecting the Poisson equation and control variates, handling the signed issue with the unbiased approximations. Interestingly, they obtain an unbiased estimator of the asymptotic variance of the unbiased estimator. And a correction for self-normalised importance sampling, which has some connections with our 1996 (?) pinball sampler. Also an evaluation of the median of means, rather than the average of means, which is a thing I had been (lazily) contemplating for a while  (On the greedy side, as I was writing my recovery exam for my Monte Carlo course, I realised the results Pierre presented could be somewhat recycled into exam problems!)

My first parallel session was on gradient-based methods with a talk by Francesca Crucinio on proximal particle Langevin algorithms (similar to the one she gave in PariSanté last year), a talk by Zhihao Wang on stereographic multiple try Metropolis(-Rosenbluth-Teller) that unsurprisingly recovers ergodicity thanks to the compactness of the ball. For which I wonder why a Normal proposal makes complete sense since one could consider a mover after the projection instead and why iid rather than repelling multiple proposals are used… The last speaker was just out from the plane from California, Siddharth Vishwanath who spoke about repelling-attracting HMC. With very nice animations of HMC, if reaching the main point of using both negative and positive frictions a few minutes before the session finished. The method preserves volume and potential, if not energy.

Speaking of which (energy), I find myself struggling with my less than 6 hours of sleep since arrival during the first afternoon session, despite a fiery hot spot lunch, which means in plainer terms that I alas dozed in and out of the talks. The second session saw Jack Jewson exposing in deeper details the exact PDMP algorithm for Gibbs measures  Jeremias Knoblauch mentioned yesterday. And Jonathan Huggins as well, using Gaussian processes as proxies for expected likelihoods, with lower guarantees than pseudo-marginal versions. In a mildly connected way, Robin Ryder went through the resolution of the ecological inference challenge they produce with Nicolas Chopin and Théo Valdoire (all authors with whom I am connected, Théo being a brillant student of our MASH Master last year and now in Harvard, hopefully till the end of his PhD!)

On the extra-academic curriculum, I had a yummy dinner in the Maxwell Hawker (street) food centre, incl. Xiao Long Bao that cooled down fast enough to avoid the usual scalding effect, plus rojak a mixed fruit and vegetable fried in a peanut sauce that I had never tasted before, popiah (ditto), chili noodles, and an appam with durian deepfried balls as a fabulous and unexpected dessert.

BayesComp 2025.1

Posted in Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on June 18, 2025 by xi'an

Minus one day at BayesComp 2025! As I am attending the model misspecification satellite workshop (ten minutes late, due to repeated path finding protocol!), with an extended presentation by Jeremias Knoblauch on post-Bayesian inference, incl. powered likelihood and Gibbs posteriors. A very smooth and pedagogical presentation, esp. in the hybrid mode. A perspective I associate with the difficulties of making sense of the post-posterior, not truly a posterior, of calibrating the penalty (eg λ), picking the loss (α, β, γ divergences?) , and the drift towards learning goals since the new measure is the post-posterior predictive. Sort of paradoxical return to a Gaussian post-posterior on the parameter that does not seem to stay robust. Horrendous computational issues, when the loss itself is an integral. Use of the zig-zag sampler with an estimated unbiased gradient of the loss, much faster than pseudo-marginal, which (naïvely?) makes sense both because PDMPs directly use scores and because of the power of stochastic gradient methods. Worse perspectives for optimisation-centric posterior that are essentially vamped versions of GANs. For instance, what is the meaning of the coverage probabilities?

The second talk by Jonathan Huggins was on DC (not bagged) posteriors as martingale posteriors with m<∞ (approximating marginal distributions with random kernel MCMC—which persists in simulating the marginalised or integrated variable u from its prior, rather than adapting to the current value of the parameter θ— or subsampling MCMC akin to stochastic gradient Langevin) with connection with cut posteriors,

Then I skipped to the second workshop on Bayesian methods for distributional and semiparametric regression, to listen to my friend David Rossell’s talk on local variable selection. Which suffers more than in standard models under misspecification. Another talk involving cut posteriors, the cuts being on the spline bases…

The day and the workshop concluded with great talks by (my friends) Pierre Alquier and David Frazier. David centred his misspecification talk on cut posteriors. Managing to bring in shrinkage estimators (and mention Bill Strawderman!).

A wee stressful trip, since the races in Caen cancelled all buses and delayed the taxi enough to miss the train to Paris by 30s, catching the next available one leaving me less than one hour between the arrival of the train (delayed by construction work on the rail line) and boarding the flight at Charles de Gaulle airport, but fortunately the RER trains in Paris were running okay, there were no queues in the airport, and I thus made it in time with a bit of post-marathon jogging! (Only to be delayed at departure by one hour for stormy conditions over Germany and Austria). All this exercise proved helpful to sleep soundly and lengthily in the plane!

robust inference using posterior bootstrap

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , on February 18, 2022 by xi'an

The famous 1994 Read Paper by Michael Newton and Adrian Raftery was entitled Approximate Bayesian inference, where the boostrap aspect is in randomly (exponentially) weighting each observation in the iid sample through a power of the corresponding density, a proposal that happened at about the same time as Tony O’Hagan suggested the related fractional Bayes factor. (The paper may also be equally famous for suggesting the harmonic mean estimator of the evidence!, although it only appeared as an appendix to the paper.) What is unclear to me is the nature of the distribution g(θ) associated with the weighted bootstrap sample, conditional on the original sample, since the outcome is the result of a random Exponential sample and of an optimisation step. With no impact of the prior (which could have been used as a penalisation factor), corrected by Michael and Adrian via an importance step involving the estimation of g(·).

At the Algorithm Seminar today in Warwick, Emilie Pompe presented recent research, including some written jointly with Pierre Jacob, [which I have not yet read] that does exactly that inclusion of the log prior as penalisation factor, along with an extra weight different from one, as motivated by the possibility of a misspecification. Including a new approach to cut models. An alternative mentioned during the talk that reminds me of GANs is to generate a pseudo-sample from the prior predictive and add it to the original sample. (Some attendees commented on the dependence of the later version on the chosen parameterisation, which is an issue that had X’ed my mind as well.)

what a party!

Posted in pictures, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , on September 13, 2021 by xi'an

We ended up having a terrific b’day party last Thursday after noon, with about 30 friends listening in Institut Henri Poincaré to Florence, Pierre, and Sylvia giving lectures on my favourite themes, namely ABC, MCMC, and mixture inference. Incl. subtle allusions to my many idiosyncrasies in three different flavours!  And a limited number of anecdotes, incl. the unavoidable Cancún glasses disaster! We later headed to a small Ethiopian restaurant located on the other side of the Panthéon, rue de l’Ecole Polytechnique (rather than on the nearby rue Laplace!), which was going to be too tiny for us, especially in these COVID times, until the sky cleared up and the restaurant set enough tables in the small street to enjoy their injeras and wots till almost midnight. The most exciting episode of the evening came when someone tried to steal some of our bags that had been stored in a back room and when Tony spotted the outlier and chased him till the thief dropped the bags..! Thanks to Tony for saving the evening and our computers!!! To Éric, Jean-Michel and Judith for organising this 9/9 event (after twisting my arm just a wee bit). And to all my friends who joined the party, some from far away…