AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback

• URL: http://arxiv.org/abs/2603.27766v1
• Date: Sun, 29 Mar 2026 16:58:46 GMT
• Title: AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback
• Abstract summary: AutoStan is a framework in which a command-line interface (CLI) coding agent autonomously builds and iteratively improves Bayesian models written in Stan.<n>We evaluate AutoStan on five datasets with diverse modeling structures.
• Score: 0.38073142980733005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present AutoStan, a framework in which a command-line interface (CLI) coding agent autonomously builds and iteratively improves Bayesian models written in Stan. The agent operates in a loop, writing a Stan model file, executing MCMC sampling, then deciding whether to keep or revert each change based on two complementary feedback signals: the negative log predictive density (NLPD) on held-out data and the sampler's own diagnostics (divergences, R-hat, effective sample size). We evaluate AutoStan on five datasets with diverse modeling structures. On a synthetic regression dataset with outliers, the agent progresses from naive linear regression to a model with Student-t robustness, nonlinear heteroscedastic structure, and an explicit contamination mixture, matching or outperforming TabPFN, a state-of-the-art black-box method, while remaining fully interpretable. Across four additional experiments, the same mechanism discovers hierarchical partial pooling, varying-slope models with correlated random effects, and a Poisson attack/defense model for soccer. No search algorithm, critic module, or domain-specific instructions are needed. This is, to our knowledge, the first demonstration that a CLI coding agent can autonomously write and iteratively improve Stan code for diverse Bayesian modeling problems.

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