[2603.27766] AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback

Computer Science > Machine Learning arXiv:2603.27766 (cs) [Submitted on 29 Mar 2026] Title:AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback Authors:Oliver Dürr View a PDF of the paper titled AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback, by Oliver D\"urr View PDF HTML (experimental) 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 ...