🧠 Check out the classic examples from Bayesian Cognitive Modeling: A Practical Course (Lee & Wagenmakers, 2013), translated into step-by-step tutorials with BayesFlow!
Interactive version: kucharssim.github.io/bayesflow-co...
PDF: osf.io/preprints/ps...
A study with 5M+ data points explores the link between cognitive parameters and socioeconomic outcomes: The stability of processing speed was the strongest predictor.
BayesFlow facilitated efficient inference for complex decision-making models, scaling Bayesian workflows to big data.
🔗Paper
Paul Bürkner (TU Dortmund University), will give our next talk. This will be about "Amortized Mixture and Multilevel Models", and is scheduled on Thursday the 30th January at 11am. To receive the link to join, sign up at listserv.csv.warwick...
making your own sourdough starter is pretty simple. Give it a go :)
Looks amazing. I recently started to bake with sourdough but with much less success...
Check out this project on modeling stationary and time-varying parameters with BayesFlow.
The family of methods is called "neural superstatistics", how can it not be cool!? 😎
👨💻 Led by @schumacherlu.bsky.social
BayesFlow is a library for amortized Bayesian inference with neural networks.
⋅ Multi-backend via Keras 3: Use PyTorch, TensorFlow, or JAX.
⋅ Modern nets: Flow matching, diffusion, consistency models, normalizing flows, transformers
⋅ Built-in diagnostics and plotting
🔗 github.com/bayesflow-or...
