High-Dimensional Simulation-Based Inference in Latent Spaces

We develop a two-stage approach that compresses high-dimensional simulator parameters, performs posterior inference in the learned latent space, and decodes samples back to the original space. Across four case studies and three generative model families, it preserves comparable accuracy and marginal calibration while sampling more than an order of magnitude faster at matched training compute.