#157 Amortized Inference & BayesFlow in Practice, with Stefan Radev

About this episode

The episode discusses amortized inference and its practical applications with guest Stefan Radev.

Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work Takeaways : Q: What is simulation-based inference and what does "sim-to-real" mean? A: Simulation-based inference (SBI) uses a mechanistic simulator as an epistemic tool: you train a neural network on a large number of labeled simulations and then deploy it on real, unlabeled data. The "sim-to-real" framing captures the key asymmetry -- your network never sees real data during training, only simulations, but it generalizes to real observations at inference time. This is the opposite of the more common "synthetic-for-ML" approach, where fake data is used purely to augment real training data. Q: What is the amortized inference agent skill and what does it do? A: It's an open-source AI agent skill, co-developed by Stefan and Alexandre, that teaches an AI coding agent to run a complete, state-of-the-art amortized inference workflow. Because amortized inference is recent enough that it's underrepresented in LLM training data, vanilla agents tend to get it wrong. The skill…

People in this episode

Host: Alexandre Andorra

Guest: Stefan Radev

Topics covered

• amortized inference
• BayesFlow
• simulation-based inference
• AI agent skills
• neural networks
• data generalization

Keywords

• amortized inference
• BayesFlow
• simulation-based inference
• AI coding agent
• neural network training
• calibration coverage

Mentioned in this episode

Books & works: Good Bayesian

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