Simulation-Based Inference: Neural Nets Solve Probability Black Boxes
burkov · x · 2026-08-25
Many scientific models can generate realistic data without providing a usable formula for observation probabilities under specific parameters, making parameter estimation difficult.
Key Contributions:
- Solution: Researchers from UCLouvain, ELLIS, and others developed a path to simulation-based inference.
- Methodology: It uses simulated examples to train neural networks that approximate the statistical quantities needed for inference.
- Unified Framework: The paper explains how this approach fits both Bayesian inference (estimating posteriors) and frequentist inference (constructing procedures with reliable long-run error rates).
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