Umberto Picchini
@uPicchiniAssociate Professor in Mathematical Statistics @StatsChalmersGU | simulation-based inference | Bayes | stochastic dynamical systems | @uPicchini.bsky.social
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new paper on simulation-based inference using Gaussian mixtures to learn likelihood and posterior, without neural networks 😱😱😱 Our SeMPLE (Sequential Mixture Posterior and Likelihood Estimation) provides excellent inference and excels when the posterior is multimodal. 1/n
Cool!
📢 Post-Bayesian online seminar series coming!📢 To stay posted, sign up at tinyurl.com/postBayes We'll discuss cutting-edge methods for posteriors that no longer rely on Bayes Theorem. (e.g., PAC-Bayes, generalised Bayes, Martingale posteriors, ...) Pls circulate widely!
for those migrating to Bluesky: these days me, as well as many others, are getting dozens of new Bluesky followers every day. But please before following anyone, write something about yourself in your profile, and have a profile pic. You are more likely to get followed-back
My new post on the place to increasingly be (and not to be) if you're interested in science, data, research, health, policy... kucharski.substack.com/p/science-soci…
A reminder of our talk tomorrow by Ullrich Köthe (University of Heidelberg) on "Free-form flows for physics-informed generative modelling". Sign up here listserv.csv.warwick.ac.uk/mailman/listin… to get the link to join.
Please follow the new X account of the Approximate Bayesian Inference seminar series, to learn of advances in simulation-based inference.
The One World Approximate Bayesian Inference (ABI) Seminar is on X. x.com/approxbayes
recent *review paper* on (neural) amortised simulation-based inference. I haven't seen it publicized much here among my connections, so there you go
Had a great time writing this review paper with former student Matthew Sainsbury-Dale and @HuserRaphael on "Neural Methods for Amortised Parameter Inference". Hope it's found as a useful resource, I definitely learned a lot writing it! #rstats arxiv.org/abs/2404.12484
tweeting this so I can also find it again. Pen and Paper exercises in ML *WITH SOLUTIONS* By Michael Gutmann arxiv.org/abs/2206.13446
Harold Jeffreys introduced the | notation for conditional probabilities. Learned from bayesianspectacles.org/the-man-who-re…
Brad Efron's 13 rules for giving a really bad talk (from efron.ckirby.su.domains/other/2013Thir…) Thanks to Robert Grant for finding this gem
a 129 pages review paper on optimal experimental design (thanks @FForbes70 for the pointer!)
Xun Huan, Jayanth Jagalur, Youssef Marzouk. [stat.ME]. Optimal experimental design: Formulations and computations. arxiv.org/abs/2407.16212
nice package for visualising of the outputs of SMC, IS and more, by @bayesian_stats For example, the pics shows the genealogy of 25 resampled particles richardgeveritt.github.io/ggsmc/articles…
" I trust this message finds you in good health and high spirits." --> mail goes into the bin
great Editorial Board for this new RSS journal academic.oup.com/rssdat/pages/e…
RSS: Data Science and Artificial Intelligence, a new fully #OpenAccess journal published on behalf of @RoyalStatSoc, is now open for submissions. Learn why the journal is a perfect fit for your data science & AI research! Discover the reasons to submit: oxford.ly/3YjTBEG
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