AI RESEARCH
GenSBI: Generative Methods for Simulation-Based Inference in JAX
arXiv CS.LG
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ArXi:2605.27499v1 Announce Type: new Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending naturally from neural posterior estimation to likelihood and joint density estimation. Their principled optimization objectives and freedom from architectural constraints have driven rapid adoption across the natural sciences. Yet the most widely used SBI libraries remain PyTorch-based, leaving researchers who develop their forward models and analysis pipelines in JAX without a native option.