Pierre-Simon Laplace

learnbayesstats.bsky.social

15 October 2026 15:39

How can we make Bayesian inference faster? 🎧 In Episode 167 @alex-andorra.bsky.social @paulbuerkner.com and Steve Bronder discuss amortized inference, neural posteriors, GPUs in Stan, and GPU-ready linear algebra. Recorded live at StanCon 2026 in Uppsala! 🔗 lnkd.in/dUTHWeq3 #BayesianStatistics

Pierre-Simon Laplace

learnbayesstats.bsky.social

09 October 2026 17:07

New episode of LBS is out! @alex-andorra.bsky.social is joined by Bill Engels & Jesse Grabowski to discuss Gaussian processes, kernels, PyTensor, and PTGP, including HSGP, computational efficiency, AI-generated statistical code & more! 🔗Watch the full episode: learnbayesstats.com/episode/gaus...

Suyog Chandramouli

suyoghc.bsky.social

24 September 2026 16:29

We kick of the visiting speakers' research seminar today at @princeton.edu with @alex-andorra.bsky.social joining us for a talk and workshop on #BayesianWorkflow and AI-assisted statistical modeling. These sessions are open to the public on zoom. For details, see the flyer below!

Pierre-Simon Laplace

learnbayesstats.bsky.social

24 September 2026 16:29

What if we learned the likelihood instead of the posterior? In Episode 165, Alex Fengler joins @alex-andorra.bsky.social to discuss HSSM, likelihood approximation networks, amortized inference and simulation-based inference and more ... 🔗 learnbayesstats.com/episode/hssm... #Bayesian #PyMC

Nathaniel Forde

nathanielforde.bsky.social

11 September 2026 09:02

For my 2 cents, the accuracy and laws of credence (great book!) stuff is a natural gateway for ML folks via loss function design. But you also have broader decision theoretic angle and i'd love to hear you discuss the rational permissivism/demands of rationality. Cc @learnbayesstats.bsky.social