GSoC 2026: Developer Overhauls Neural Network Config API for Bayesian Inference Library sbi

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A Google Summer of Code 2026 contributor spent the summer redesigning how the sbi library — a Python tool for simulation-based Bayesian inference — configures its neural network estimators. The project replaced sbi's fragmented string-and-factory-function interface with typed, frozen dataclass configuration objects, one per model family, covering all supported estimator types including NPE, NLE, NRE, FMPE, and NPSE. The new API catches misconfigured settings immediately at configuration time rather than silently discarding them during training, while a deprecation path ensures existing code continues to work. Key changes were merged across pull requests #1872, #1877, and #1882, with mentorship from Jan Teusen and Nicholas Junge under the NumFOCUS umbrella organisation. The updated interface improves editor autocomplete support, enables clean logging of experiment configurations, and eliminates an entire class of silent hyperparameter errors.

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