Lukas Schumacher
@schumacherlu.bsky.social
Postdoctoral Researcher at University of Basel
Interested in Cognitive Modeling | Decision Making | Dynamics in Cognition | Amortized Bayesian Inference | Superstatistics
Interested in Cognitive Modeling | Decision Making | Dynamics in Cognition | Amortized Bayesian Inference | Superstatistics
Reposted by Lukas Schumacher
🧠 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...
Interactive version: kucharssim.github.io/bayesflow-co...
PDF: osf.io/preprints/ps...
Introduction – Amortized Bayesian Cognitive Modeling
kucharssim.github.io
May 30, 2025 at 2:28 PM
🧠 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...
Interactive version: kucharssim.github.io/bayesflow-co...
PDF: osf.io/preprints/ps...
Reposted by Lukas Schumacher
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
BayesFlow facilitated efficient inference for complex decision-making models, scaling Bayesian workflows to big data.
🔗Paper
February 3, 2025 at 12:21 PM
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
BayesFlow facilitated efficient inference for complex decision-making models, scaling Bayesian workflows to big data.
🔗Paper
Reposted by Lukas Schumacher
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...
January 14, 2025 at 12:00 PM
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...
Reposted by Lukas Schumacher
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
The family of methods is called "neural superstatistics", how can it not be cool!? 😎
👨💻 Led by @schumacherlu.bsky.social
Neural superstatistics are a framework for probabilistic models with time-varying parameters:
⋅ Joint estimation of stationary and time-varying parameters
⋅ Amortized parameter inference and model comparison
⋅ Multi-horizon predictions and leave-future-out CV
📄 Paper 1
📄 Paper 2
💻 BayesFlow Code
⋅ Joint estimation of stationary and time-varying parameters
⋅ Amortized parameter inference and model comparison
⋅ Multi-horizon predictions and leave-future-out CV
📄 Paper 1
📄 Paper 2
💻 BayesFlow Code
December 6, 2024 at 12:25 PM
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
The family of methods is called "neural superstatistics", how can it not be cool!? 😎
👨💻 Led by @schumacherlu.bsky.social
Reposted by Lukas Schumacher
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...
⋅ 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...
November 22, 2024 at 10:31 PM
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...
⋅ 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...