Archive for Orne
semi-marathon d’Argentan [30th year]
Posted in pictures, Running, Travel with tags Argentan, Argentan half-marathon, bocage, Caen, countryside, Courir en Normandie, forêt de Gouffern, half-marathon, Insee Paris Club, M6M, Orne, road race on October 3, 2026 by xi'an
off to Caen for the Pegasus half-marathon [39th edition]
Posted in Statistics with tags 21.1km, bagpipes, Battle of Normandy, Caen, Courants de la Liberté, D Day, half-marathon, La Rochambelle, Normandy Running Festival, Orne, Pegasus bridge on June 6, 2026 by xi'an
Amy half does Argentan [1:36:03, +29:22, 79/398, 1/21 M5+6H, 15⁰, 123D⁺]
Posted in pictures, Running with tags Argentan, Argentan half-marathon, carbon plate, Courir en Normandie, extratropical cyclone, hail, half-marathon, Jakob Ingebrigtsen, M6M, Nike Vaporfly, Nike Vaporfly Next% 4, Normandie fort et vert, Normandy, Orne, podium, road race, storm, wind on October 8, 2025 by xi'an
Argentan semi (39th)
Posted in pictures, Running, Travel with tags Argentan, Argentan half-marathon, Courir en Normandie, half-marathon, hamstring injury, M6M, Normandie fort et vert, Normandy, Orne, road race, xianblog on October 4, 2025 by xi'an
BayesComp 2025.1
Posted in Running, Statistics, Travel, University life with tags BayesComp 2025, Bayesian GANs, Bayesian neural networks, Bayesian predictive, Bayesian robustness, Bayesian semi-parametrics, Bénouville, Caen, CDG, cut distribution, cut models, cut posterior, equator, Gibbs posterior, half-marathon, Les Courants de la Liberté, local regression, National University Singapore, Normandie, NUS, Orne, PDMP, Pegasus bridge, power posterior, random kernel MCMC, RATP, RER, safe Bayes, shrinkage estimation, Singapore, SNCF, splines, Stein divergence, stochastic gradient MCMC, taxi, train travel, William Strawderman, zigzag algorithm 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!
