Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation
Researchers propose a framework that integrates large language models for program synthesis with neural simulation-based inference, enabling both model selection and parameter estimation from natural language descriptions. The system generates and iteratively refines candidate simulator programs, which are then evaluated using neural density estimation. The method is demonstrated on benchmarks including deterministic dynamics, epidemic models, and dark matter inference, showing the ability to identify plausible model families from open-ended prompts.
Why it matters: This approach broadens the scope of simulation-based inference by allowing automated exploration over multiple model structures, potentially accelerating scientific discovery in complex domains.
Full story at: arXiv Statistical ML ↗