Simulations are no longer just “nice to have.” They’re reshaping how we do statistics.

Care to learn more? Check out our paper arxiv.org/abs/2503.24011, accepted for publication in the upcoming theme issue of Philosophical Transactions A.

BayesFlow released version 2.0.4, presented numerous findings at the MathPsych/ICCM 2025 conference at Ohio State University, and expanded its contributor list to 25 active members! Congrats to BayesFlow on all these new huge accomplishments!

🧠 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...

Finite mixture models are useful when data comes from multiple latent processes.

BayesFlow allows:
• Approximating the joint posterior of model parameters and mixture indicators
• Inferences for independent and dependent mixtures
• Amortization for fast and accurate estimation

📄 Preprint
💻 Code

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...

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

Join us this Thursday for a talk on efficient mixture and multilevel models with neural networks by @paulbuerkner.com at the new @approxbayesseminar.bsky.social!

1️⃣ An agent-based model simulates a dynamic population of professional speed climbers.
2️⃣ BayesFlow handles amortized parameter estimation in the SBI setting.

📣 Shoutout to @masonyoungblood.bsky.social & @sampassmore.bsky.social

📄 Preprint: osf.io/preprints/ps...
💻 Code: github.com/masonyoungbl...

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

Any single analysis hides an iceberg of uncertainty.

Sensitivity-aware amortized inference explores the iceberg:
⋅ Test alternative priors, likelihoods, and data perturbations
⋅ Deep ensembles flag misspecification issues
⋅ No model refits required during inference

🔗 openreview.net/forum?id=Kxt...

To celebrate the new beginnings on Bluesky, let's reminisce about one of our highlights from the old days:

The unexpected shout-out by @fchollet.bsky.social that made everyone go crazy on the BayesFlow Slack server and led to a 15% increase in GitHub stars.

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...